{"id":13409283,"url":"https://github.com/gjy3035/Awesome-Crowd-Counting","last_synced_at":"2025-03-14T14:31:07.368Z","repository":{"id":40643685,"uuid":"130979291","full_name":"gjy3035/Awesome-Crowd-Counting","owner":"gjy3035","description":"Awesome Crowd Counting","archived":false,"fork":false,"pushed_at":"2024-05-21T13:50:26.000Z","size":1146,"stargazers_count":2310,"open_issues_count":16,"forks_count":468,"subscribers_count":103,"default_branch":"master","last_synced_at":"2024-05-21T15:08:55.106Z","etag":null,"topics":["computer-vision","crowd-analysis","crowd-counting"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gjy3035.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-04-25T09:01:25.000Z","updated_at":"2024-05-29T04:34:39.378Z","dependencies_parsed_at":"2023-02-19T06:01:46.950Z","dependency_job_id":"9852c9f2-4ad0-4809-bf46-11ab8a95b2cb","html_url":"https://github.com/gjy3035/Awesome-Crowd-Counting","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gjy3035%2FAwesome-Crowd-Counting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gjy3035%2FAwesome-Crowd-Counting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gjy3035%2FAwesome-Crowd-Counting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gjy3035%2FAwesome-Crowd-Counting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gjy3035","download_url":"https://codeload.github.com/gjy3035/Awesome-Crowd-Counting/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243593330,"owners_count":20316168,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","crowd-analysis","crowd-counting"],"created_at":"2024-07-30T20:00:59.577Z","updated_at":"2025-03-14T14:31:07.340Z","avatar_url":"https://github.com/gjy3035.png","language":null,"funding_links":[],"categories":["Recommended Readings","Uncategorized","Others","CV","Papers","Misc","Multimodal, Vision-Language, and Generative AI","Other Lists","Pedestrain/Crowd","Awesome Computer Vision"],"sub_categories":["UCF-QNRF Large Crowd Counting Dataset","Uncategorized","Acknowledgement","Relaetd Tasks","Computer Vision","TeX Lists","3D SemanticSeg"],"readme":"# Awesome Crowd Counting[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n\nIf you have any problems, suggestions or improvements, please submit the issue or PR.\n\n## Contents\n* [Misc](#misc)\n* [Datasets](#datasets)\n* [Papers](#papers)\n* [Leaderboard](#leaderboard)\n\n## Misc\n\n### News\n- [2022.09] The VSCrowd Dataset is released.\n- [2022.01] The FUDAN-UCC Dataset is released.\n- [2021.04] The RGBT-CC Benchmark is released.\n- [2020.04] The JHU-CROWD++ Dataset is released.\n- [2020.01] The NWPU-Crowd benchmark is released.\n\n### Call for Papers\n- [Electronics] Special Issue on: Recent Advances in Pixel-Wise Image Understanding [[Link](https://www.mdpi.com/journal/electronics/special_issues/Pixel-Wise_Image_Understanding)]. Deadline: November 15, 2023.\n- [Transportation Research Part C] ~~Special Issue on: Applications of artificial intelligence, computer vision, physics and econometrics modelling methods in pedestrian traffic modelling and crowd safety~~ [[Link](https://www.sciencedirect.com/journal/transportation-research-part-c-emerging-technologies/about/call-for-papers#call-for-papers-applications-of-artificial-intelligence-computer-vision-physics-and-econometrics-modelling-methods-in-pedestrian-traffic-modelling-and-crowd-safety)]. Deadline: April 30th, 2023.\n- [IET Image Processing] ~~Special Issue on: Crowd Understanding and Analysis~~ [[Link](https://digital-library.theiet.org/content/journals/iet-ipr/info/spl-issues;jsessionid=rfnb4mhi25p6.x-iet-live-01)] [[PDF](https://digital-library.theiet.org/files/IET_IPR_CFP_CUA.pdf)]\n\n### Challenge\n- [[NWPU-Crowd Counting](https://www.crowdbenchmark.com/nwpucrowd.html)] Crowd counting. Deadline: none.\n- [[VisDrone 2021](http://aiskyeye.com/challenge_2021/crowd-counting-2/)] ~~Crowd counting. ICCV Workshop. Deadline: **2021.07.15**.~~\n- [[VisDrone 2020](http://aiskyeye.com/challenge/crowd-counting/)] ~~Crowd counting. ECCV Workshop. Deadline: **2020.07.15**.~~\n\n### Code\n- [[C^3 Framework](https://github.com/gjy3035/C-3-Framework)] An open-source PyTorch code for crowd counting, which is released. ![GitHub stars](http://img.shields.io/github/stars/gjy3035/C-3-Framework.svg?logo=github\u0026label=Stars)\n- [[CCLabeler](https://github.com/Elin24/cclabeler)] A web tool for labeling pedestrians in an image, which is released. ![GitHub stars](http://img.shields.io/github/stars/Elin24/cclabeler.svg?logo=github\u0026label=Stars)\n- [[YOLO-CROWD](https://github.com/zaki1003/YOLO-CROWD)] ![GitHub stars](http://img.shields.io/github/stars/zaki1003/YOLO-CROWD.svg?logo=github\u0026label=Stars) a lightweight crowd counting and face detection model that is based on [[YOLO-FaceV2](https://github.com/Krasjet-Yu/YOLO-FaceV2)] ![GitHub stars](http://img.shields.io/github/stars/Krasjet-Yu/YOLO-FaceV2.svg?logo=github\u0026label=Stars)\n\n### Technical blog\n- [Chinese Blog] 人群计数论文解读 [[Link](https://zhuanlan.zhihu.com/c_1111215695622352896)]\n- [2019.05] [Chinese Blog] C^3 Framework系列之一：一个基于PyTorch的开源人群计数框架 [[Link](https://zhuanlan.zhihu.com/p/65650998)]\n- [2019.04] Crowd counting from scratch [[Link](https://github.com/CommissarMa/Crowd_counting_from_scratch)]\n- [2017.11] Counting Crowds and Lines with AI [[Link1](https://blog.dimroc.com/2017/11/19/counting-crowds-and-lines/)] [[Link2](https://count.dimroc.com/)] [[Code](https://github.com/dimroc/count)]![GitHub stars](http://img.shields.io/github/stars/dimroc/count.svg?logo=github\u0026label=Stars)\n\n###  GT generation\n- Density Map Generation from Key Points [[Matlab Code](https://github.com/aachenhang/crowdcount-mcnn/tree/master/data_preparation)] [[Python Code](https://github.com/leeyeehoo/CSRNet-pytorch/blob/master/make_dataset.ipynb)] [[Fast Python Code](https://github.com/vlad3996/computing-density-maps)] [[Pytorch CUDA Code](https://github.com/gjy3035/NWPU-Crowd-Sample-Code/blob/master/misc/dot_ops.py)]\n\n### Related Tasks\nCrowd Analysis, [Crowd Localization](https://github.com/taohan10200/Awesome-Crowd-Localization), [Video Surveillance](https://github.com/CommissarMa/Awesome-Public-Safety-in-Vision), Dense/Small/Tiny Object Detection\n\n## Datasets\n\nPlease refer to [this page](src/Datasets.md).\n\n## Papers\n\nConsidering the increasing number of papers in this field, we roughly summarize some articles and put them into the following categories (they are still listed in this document):\n\n| [[**Top Conference/Journal**](src/Top_Conference-Journal.md)] | [[**Survey**](src/Survey.md)] | [[**Un-/semi-/weakly-/self- Supervised Learning**](src/Un-_Semi-_Weakly-_Self-_supervised_Learning.md)] |\n| :---- | :---- | :---- |\n| [[**Auxiliary Tasks**](src/Auxiliary_Tasks.md)] | [[**Localization**](src/Localization.md)] | [[**Transfer Learning and Domain Adaptation**](src/Transfer_Learning_and_Domain_Adaptation.md)] |\n| [[**Light-weight Models**](src/Light-weight_Model.md)] | [[**Video**](src/Video.md)] | [[**Network Design, Search**](src/Network_Design_and_Search.md)] |\n| [[**Perspective Map**](src/Perspective_Map.md)] | [[**Attention**](src/Attention.md)] | [[**Transformer**](src/Transformer.md)] |\n\n### arXiv papers\nNote that all unpublished arXiv papers are not included in [the leaderboard of performance](#performance).\n\n- Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark [[paper](https://arxiv.org/abs/2411.13056)]\n- Bound Tightening Network for Robust Crowd Counting [[paper](https://arxiv.org/abs/2409.19146)]\n- Boosting Adverse Weather Crowd Counting via Multi-queue Contrastive Learning [[paper](https://arxiv.org/abs/2408.05956)]\n- VMambaCC: A Visual State Space Model for Crowd Counting [[paper](https://arxiv.org/abs/2405.03978)]\n- Fuss-Free Network: A Simplified and Efficient Neural Network for Crowd Counting [[paper](https://arxiv.org/abs/2404.07847)]\n- CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification [[paper](https://arxiv.org/abs/2403.09281v1)][[code](https://github.com/Yiming-M/CLIP-EBC)] ![GitHub stars](http://img.shields.io/github/stars/Yiming-M/CLIP-EBC.svg?logo=github\u0026label=Stars)\n- Robust Unsupervised Crowd Counting and Localization with Adaptive Resolution SAM [[paper](https://arxiv.org/abs/2402.17514)]\n- Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling [[paper](https://arxiv.org/abs/2402.15297)]\n- Diffusion-based Data Augmentation for Object Counting Problems [[paper](https://arxiv.org/abs/2401.13992)]\n- A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting [[paper](https://arxiv.org/abs/2401.05968)]\n- Scale-Aware Crowd Count Network with Annotation Error Correction [[paper](https://arxiv.org/abs/2312.16771)]\n- SYRAC: Synthesize, Rank, and Count [[paper](https://arxiv.org/abs/2310.01662)]\n- Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement [[paper](https://arxiv.org/abs/2305.09271)]\n- CLIP-Count: Towards Text-Guided Zero-Shot Object Counting [[paper](https://arxiv.org/abs/2305.07304)]\n- Can SAM Count Anything? An Empirical Study on SAM Counting [[paper](https://arxiv.org/abs/2304.10817)]\n- Why Existing Multimodal Crowd Counting Datasets Can Lead to Unfulfilled Expectations in Real-World Applications [[paper](https://arxiv.org/abs/2304.06401)]\n- Crowd Counting with Sparse Annotation [[paper](https://arxiv.org/abs/2304.06021)]\n- Crowd Counting with Online Knowledge Learning [[paper](https://arxiv.org/abs/2303.10318)]\n- LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance [[paper](https://arxiv.org/abs/2302.05374)]\n- Mask Focal Loss for dense crowd counting with canonical object detection networks [[paper](https://arxiv.org/abs/2212.11542)]\n- CountingMOT: Joint Counting, Detection and Re-Identification for Multiple Object Tracking [[paper](https://arxiv.org/abs/2212.05861)]\n- Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects [[paper](https://arxiv.org/abs/2212.02248)]\n\n\n\u003cdetails\u003e\n\u003csummary\u003eEarlier ArXiv Papers\u003c/summary\u003e\n\n- Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network [[paper](https://arxiv.org/abs/2211.06835)]\n- Inception-Based Crowd Counting -- Being Fast while Remaining Accurate [[paper](https://arxiv.org/abs/2210.09796)]\n- Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training [[paper](https://arxiv.org/abs/2208.07075)]\n- MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting [[paper](https://arxiv.org/abs/2208.06761)]\n- Multi-scale Feature Aggregation for Crowd Counting [[paper](https://arxiv.org/abs/2208.05256)]\n- Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications [[paper](https://arxiv.org/abs/2207.10155)]\n- Indirect-Instant Attention Optimization for Crowd Counting in Dense Scenes [[paper](https://arxiv.org/abs/2206.05648)]\n- Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd Counting [[paper](https://arxiv.org/abs/2206.05475)]\n- Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches [[paper](https://arxiv.org/abs/2204.04653)]\n- Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting [[paper](https://arxiv.org/abs/2203.06388)]\n- Counting with Adaptive Auxiliary Learning [[paper](https://arxiv.org/abs/2203.04061)][[code](https://github.com/smallmax00/Counting_With_Adaptive_Auxiliary_Learning)]![GitHub stars](http://img.shields.io/github/stars/smallmax00/Counting_With_Adaptive_Auxiliary_Learning.svg?logo=github\u0026label=Stars)\n- CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability [[paper](https://arxiv.org/abs/2203.03768)]\n- S2FPR: Crowd Counting via Self-Supervised Coarse to Fine Feature Pyramid Ranking [[paper](https://arxiv.org/abs/2201.04819)][[code](https://github.com/bridgeqiqi/S2FPR)]![GitHub stars](http://img.shields.io/github/stars/bridgeqiqi/S2FPR.svg?logo=github\u0026label=Stars)\n- Scene-Adaptive Attention Network for Crowd Counting [[paper](https://arxiv.org/abs/2112.15509)]\n- Object Counting: You Only Need to Look at One [[paper](https://arxiv.org/abs/2112.05993)]\n- PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting [[paper](https://arxiv.org/abs/2111.00406)]\n- LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds [[paper](https://arxiv.org/abs/2110.04727)]\n- CCTrans: Simplifying and Improving Crowd Counting with Transformer [[paper](https://arxiv.org/abs/2109.14483)]\n- S4-Crowd: Semi-Supervised Learning with Self-Supervised Regularisation for Crowd Counting [[paper](https://arxiv.org/abs/2108.13969)]\n- Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation [[paper](https://arxiv.org/abs/2108.02980)]\n- Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting [[paper](https://arxiv.org/abs/2108.02970)]\n- Multi-Level Attentive Convoluntional Neural Network for Crowd Counting [[paper](https://arxiv.org/abs/2105.11422)]\n- Boosting Crowd Counting with Transformers [[paper](https://arxiv.org/abs/2105.10926)]\n- Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification [[paper](https://arxiv.org/abs/2105.09684)]\n- WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting [[paper](https://arxiv.org/abs/2103.09408)]\n- Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting [[paper](https://arxiv.org/abs/2104.13946)]\n- Multi-channel Deep Supervision for Crowd Counting [[paper](https://arxiv.org/abs/2103.09553)]\n- Enhanced Information Fusion Network for Crowd Counting [[paper](https://arxiv.org/abs/2101.01479)]\n- Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background [[paper](https://arxiv.org/abs/2101.04279)]\n- Learning Independent Instance Maps for Crowd Localization [[paper](https://arxiv.org/abs/2012.04164)] [[code](https://github.com/taohan10200/IIM)]![GitHub stars](http://img.shields.io/github/stars/taohan10200/IIM.svg?logo=github\u0026label=Stars)\n- A Strong Baseline for Crowd Counting and Unsupervised People Localization [[paper](https://arxiv.org/abs/2011.03725)]\n- A Study of Human Gaze Behavior During Visual Crowd Counting [[paper](https://arxiv.org/abs/2009.06502)]\n- Bayesian Multi Scale Neural Network for Crowd Counting [[paper](https://arxiv.org/abs/2007.14245)]\n- Dense Crowds Detection and Counting with a Lightweight Architecture [[paper](https://arxiv.org/abs/2007.06630)]\n- Exploit the potential of Multi-column architecture for Crowd Counting [[paper](https://arxiv.org/abs/2007.05779)][[code](https://github.com/JunhaoCheng/Pyramid_Scale_Network)]![GitHub stars](http://img.shields.io/github/stars/JunhaoCheng/Pyramid_Scale_Network.svg?logo=github\u0026label=Stars)\n- Recurrent Distillation based Crowd Counting [[paper](https://arxiv.org/abs/2006.07755)]\n- Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions [[paper](https://arxiv.org/abs/2005.07097)][[code](https://github.com/qingzwang/AudioVisualCrowdCounting)]![GitHub stars](http://img.shields.io/github/stars/qingzwang/AudioVisualCrowdCounting.svg?logo=github\u0026label=Stars)\n- CNN-based Density Estimation and Crowd Counting: A Survey [[paper](https://arxiv.org/abs/2003.12783)]\n- Drone Based RGBT Vehicle Detection and Counting: A Challenge [[paper](https://arxiv.org/abs/2003.02437)]\n- Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network [[paper](https://arxiv.org/abs/1912.01811)][[code](https://github.com/VisDrone)]\n- Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting [[paper](https://arxiv.org/abs/1911.11484)]\n- Content-aware Density Map for Crowd Counting and Density Estimation [[paper](https://arxiv.org/abs/1906.07258)]\n- Crowd Transformer Network [[paper](https://arxiv.org/abs/1904.02774)]\n- W-Net: Reinforced U-Net for Density Map Estimation [[paper](https://arxiv.org/abs/1903.11249)][[code](https://github.com/ZhengPeng7/W-Net-Keras)]![GitHub stars](http://img.shields.io/github/stars/ZhengPeng7/W-Net-Keras.svg?logo=github\u0026label=Stars)\n- Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting [[paper](https://arxiv.org/abs/1902.01115)]\n- Scale-Aware Attention Network for Crowd Counting [[paper](https://arxiv.org/abs/1901.06026)]\n- Crowd Counting with Density Adaption Networks [[paper](https://arxiv.org/abs/1806.10040)]\n- Improving Object Counting with Heatmap Regulation [[paper](https://arxiv.org/abs/1803.05494)][[code](https://github.com/littleaich/heatmap-regulation)]![GitHub stars](http://img.shields.io/github/stars/littleaich/heatmap-regulation.svg?logo=github\u0026label=Stars)\n- Structured Inhomogeneous Density Map Learning for Crowd Counting [[paper](https://arxiv.org/abs/1801.06642)]\n\n\u003c/details\u003e\n\n## 2025\n### Conference\n- \u003ca name=\"OmniCount\"\u003e\u003c/a\u003eOmniCount: Multi-label Object Counting with Semantic-Geometric Priors (**AAAI**)[[paper](https://arxiv.org/abs/2403.05435)][[web](https://mondalanindya.github.io/OmniCount/)]\n\n### 2024\n### Conference\n- \u003ca name=\"SVCW\"\u003e\u003c/a\u003e Multi-view People Detection in Large Scenes via Supervised View-wise Contribution Weighting (**AAAI**)[[paper](https://arxiv.org/abs/2405.19943)][[code](https://github.com/zqyq/Multi-view-People-Detection-in-Large-Scenes-via-Supervised-View-wise-Contribution-Weighting)]\n- \u003ca name=\"\"\u003e\u003c/a\u003e Boosting Semi-supervised Crowd Counting with Scale-based Active Learning (**ACM MM**)[[paper](https://openreview.net/pdf?id=AXvAc3HQFl)]\n- \u003ca name=\"\"\u003e\u003c/a\u003e Domain-Agnostic Crowd Counting via Uncertainty-Guided Style Diversity Augmentation (**ACM MM**)[[paper](https://openreview.net/attachment?id=nFrcliTxAC\u0026name=pdf)]\n- \u003ca name=\"ME\"\u003e\u003c/a\u003e**[ME]** Multi-modal Crowd Counting via Modal Emulation (**BMVC**)[[paper](https://arxiv.org/abs/2407.19491)][[code](https://github.com/Mr-Monday/Multi-modal-Crowd-Counting-via-Modal-Emulation)]![GitHub stars](http://img.shields.io/github/stars/Mr-Monday/Multi-modal-Crowd-Counting-via-Modal-Emulation.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"BM\"\u003e\u003c/a\u003e**[BM]** Multi-modal Crowd Counting via a Broker Modality (**ECCV**)[[paper](https://arxiv.org/abs/2407.07518)][[code](https://github.com/HenryCilence/Broker-Modality-Crowd-Counting)]![GitHub stars](http://img.shields.io/github/stars/HenryCilence/Broker-Modality-Crowd-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CountFormer\"\u003e\u003c/a\u003e**[CountFormer]** CountFormer: Multi-View Crowd Counting Transformer (**ECCV**)[[paper](https://arxiv.org/abs/2407.02047)]\n- \u003ca name=\"APGCC\"\u003e\u003c/a\u003e**[APGCC]** Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance (**ECCV**)[[paper](https://arxiv.org/abs/2405.10589)][[code](https://github.com/AaronCIH/APGCC)]![GitHub stars](http://img.shields.io/github/stars/AaronCIH/APGCC.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"OALNet\"\u003e\u003c/a\u003e**[OALNet]** Few-shot Class-agnostic Counting with Occlusion Augmentation and Localization (**ISCAS**)[[paper](https://ieeexplore.ieee.org/document/10558069)]\n- \u003ca name=\"WSCC_TAF\"\u003e\u003c/a\u003e**[WSCC_TAF]** Weakly-Supervised Crowd Counting with Token Attention and Fusion: A Simple and Effective Baseline (**ICASSP**) [[paper](https://ieeexplore.ieee.org/document/10446636)][[code](https://github.com/WangyiNTU/WSCC_TAF)]![GitHub stars](http://img.shields.io/github/stars/WangyiNTU/WSCC_TAF.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CrowdDiff\"\u003e\u003c/a\u003e**[CrowdDiff]** CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion Models (**CVPR**) [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Ranasinghe_CrowdDiff_Multi-hypothesis_Crowd_Density_Estimation_using_Diffusion_Models_CVPR_2024_paper.pdf)][[code](https://github.com/dylran/crowddiff)]![GitHub stars](http://img.shields.io/github/stars/dylran/crowddiff.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"PseCo\"\u003e\u003c/a\u003e**[PseCo]** Point, Segment and Count: A Generalized Framework for Object Counting [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_Point_Segment_and_Count_A_Generalized_Framework_for_Object_Counting_CVPR_2024_paper.pdf)][[code](https://github.com/Hzzone/PseCo)]![GitHub stars](http://img.shields.io/github/stars/Hzzone/PseCo.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"mPrompt\"\u003e\u003c/a\u003e**[mPrompt]** Regressor-Segmenter Mutual Prompt Learning for Crowd Counting (**CVPR**) [[paper](https://arxiv.org/abs/2312.01711)]\n- \u003ca name=\"MPCount\"\u003e\u003c/a\u003e**[MPCount]** Single Domain Generalization for Crowd Counting (**CVPR**) [[paper](https://arxiv.org/abs/2403.09124)][[code](https://github.com/Shimmer93/MPCount)]\n- \u003ca name=\"Gramformer\"\u003e\u003c/a\u003e**[Gramformer]** Gramformer: Learning Crowd Counting via Graph-Modulated Transformer (**AAAI**) [[paper](https://arxiv.org/abs/2401.03870)][[code](https://github.com/LoraLinH/Gramformer)]![GitHub stars](http://img.shields.io/github/stars/LoraLinH/Gramformer.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SRN\"\u003e\u003c/a\u003e**[SRN]** Glance To Count: Learning To Rank With Anchors for Weakly-Supervised Crowd Counting (**WACV**)[[paper](https://openaccess.thecvf.com/content/WACV2024/papers/Xiong_Glance_To_Count_Learning_To_Rank_With_Anchors_for_Weakly-Supervised_WACV_2024_paper.pdf)][[code](https://github.com/pandaszzzzz/CCRanking)]![GitHub stars](http://img.shields.io/github/stars/pandaszzzzz/CCRanking.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SRN\"\u003e\u003c/a\u003e**[SAM]** Training-free Object Counting with Prompts (**WACV**)[[paper](https://openaccess.thecvf.com/content/WACV2024/papers/Shi_Training-Free_Object_Counting_With_Prompts_WACV_2024_paper.pdf)][[code](https://github.com/shizenglin/training-free-object-counter)]![GitHub stars](http://img.shields.io/github/stars/shizenglin/training-free-object-counter.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SRN\"\u003e\u003c/a\u003e**[SGA]** Semantic Generative Augmentations for Few-Shot Counting (**WACV**)[[paper](https://openaccess.thecvf.com/content/WACV2024/papers/Doubinsky_Semantic_Generative_Augmentations_for_Few-Shot_Counting_WACV_2024_paper.pdf)]\n\n### Journal\n- \u003ca name=\"Multimodal-SDA\"\u003e\u003c/a\u003e**[Multimodal-SDA]** A three-stream fusion and self-differential attention network for multi-modal crowd counting (**Pattern Recognition Letters**) [[paper](https://www.sciencedirect.com/science/article/pii/S016786552400134X)]\n- Focus for Free in Density-Based Counting (**IJCV**) [[paper](https://arxiv.org/abs/2306.05129)][[code](https://github.com/shizenglin/Counting-with-Focus-for-Free)] ![GitHub stars](http://img.shields.io/github/stars/shizenglin/Counting-with-Focus-for-Free.svg?logo=github\u0026label=Stars)(extension of [CFF](#CFF))\n- \u003ca name=\"MDKNet\"\u003e\u003c/a\u003e**[MDKNet]** Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting (**T-NNLS**) [[paper](https://arxiv.org/abs/2402.03758)][[code](https://github.com/csguomy/MDKNet)]![GitHub stars](http://img.shields.io/github/stars/csguomy/MDKNet.svg?logo=github\u0026label=Stars)\n- Rethinking Global Context in Crowd Counting (**MIR**) [[paper](https://link.springer.com/article/10.1007/s11633-023-1475-z)]\n- \u003ca name=\"HPS\"\u003e\u003c/a\u003e**[HPS]** Hybrid Perturbation Strategy for Semi-Supervised Crowd Counting (**TIP**) [[paper](https://arxiv.org/abs/2402.03758)]\n- \u003ca name=\"LDFNet\"\u003e\u003c/a\u003e**[LDFNet]** Learning Discriminative Features for Crowd Counting (**TIP**) [[paper](https://arxiv.org/abs/2311.04509)]\n- \u003ca name=\"HKINet\"\u003e\u003c/a\u003e**[HKINet]** Hierarchical Kernel Interaction Network for Remote Sensing Object Counting (**TGRS**) [[paper](https://ieeexplore.ieee.org/abstract/document/10397559)]\n- \u003ca name=\"MRC-Crowd\"\u003e\u003c/a\u003e**[MRC-Crowd]** Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes (**T-CSVT**) [[paper](https://arxiv.org/abs/2310.10352)][[code](https://github.com/cha15yq/MRC-Crowd)]![GitHub stars](http://img.shields.io/github/stars/cha15yq/MRC-Crowd.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"GCNet\"\u003e\u003c/a\u003e**[GCNet]** GCNet: Probing Self-Similarity Learning for Generalized Counting Network (**Pattern Recognition**) [[paper](https://arxiv.org/abs/2302.05132)]\n\n### 2023\n### Conference\n- \u003ca name=\"Crowd-Hat\"\u003e\u003c/a\u003e**[Crowd-Hat]** Boosting Detection in Crowd Analysis via Underutilized Output Features (**CVPR**)[[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Boosting_Detection_in_Crowd_Analysis_via_Underutilized_Output_Features_CVPR_2023_paper.pdf)][[code](https://github.com/wskingdom/Crowd-Hat)]![GitHub stars](http://img.shields.io/github/stars/wskingdom/Crowd-Hat.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"PET\"\u003e\u003c/a\u003e**[STEERER]** STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning (**ICCV**)[[paper](https://arxiv.org/abs/2308.10468)][[code](https://github.com/taohan10200/STEERER)]![GitHub stars](http://img.shields.io/github/stars/taohan10200/STEERER.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"PET\"\u003e\u003c/a\u003e**[PET]** Point-Query Quadtree for Crowd Counting, Localization, and More (**ICCV**)[[paper](https://arxiv.org/abs/2308.13814)][[code](https://github.com/cxliu0/PET)]![GitHub stars](http://img.shields.io/github/stars/cxliu0/PET.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"\"\u003e\u003c/a\u003eStriking a Balance: Unsupervised Cross-Domain Crowd Counting via Knowledge Diffusion (**ACM MM**)[[paper](http://aim-nercms.whu.edu.cn/news/list-39.html)]\n- \u003ca name=\"AWCC-Net\"\u003e\u003c/a\u003e**[AWCC-Net]** Counting Crowds in Bad Weather (**ICCV**)[[paper](https://arxiv.org/abs/2306.01209)][[code](https://github.com/awccnet/AWCC-Net)]![GitHub stars](http://img.shields.io/github/stars/awccnet/AWCC-Net.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CU\"\u003e\u003c/a\u003e**[CU]** Calibrating Uncertainty for Semi-Supervised Crowd Counting (**ICCV**)[[paper](https://arxiv.org/abs/2308.09887)][[code](https://github.com/superlc1995/Calibrating_count)]![GitHub stars](http://img.shields.io/github/stars/superlc1995/Calibrating_count.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DAOT\"\u003e\u003c/a\u003e**[DAOT]** DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting (**ACM MM**)[[paper](https://arxiv.org/abs/2308.05311)]\n- \u003ca name=\"ZSC\"\u003e\u003c/a\u003e**[ZSC]** Zero-shot Object Counting (**CVPR**)[[paper](https://arxiv.org/abs/2303.02001)][[code](https://github.com/cvlab-stonybrook/zero-shot-counting)]![GitHub stars](http://img.shields.io/github/stars/cvlab-stonybrook/zero-shot-counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DDC\"\u003e\u003c/a\u003e**[DDC]** Diffuse-Denoise-Count: Accurate Crowd-Counting with Diffusion Models (**CVPR**)[[paper](https://arxiv.org/abs/2303.12790)][[code](https://github.com/dylran/DiffuseDenoiseCount)]![GitHub stars](http://img.shields.io/github/stars/dylran/DiffuseDenoiseCount.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"IOCFormer\"\u003e\u003c/a\u003e**[IOCFormer]** Indiscernible Object Counting in Underwater Scenes (**CVPR**)[[paper](http://arxiv.org/abs/2304.11677)][[code](https://github.com/GuoleiSun/Indiscernible-Object-Counting)]![GitHub stars](http://img.shields.io/github/stars/GuoleiSun/Indiscernible-Object-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CrowdCLIP\"\u003e\u003c/a\u003e**[CrowdCLIP]** CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model (**CVPR**)[[paper](https://arxiv.org/abs/2304.04231)]\n- \u003ca name=\"OT-M\"\u003e\u003c/a\u003e**[OT-M]** Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised Counting (**CVPR**)[[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Optimal_Transport_Minimization_Crowd_Localization_on_Density_Maps_for_Semi-Supervised_CVPR_2023_paper.pdf)][[code](https://github.com/Elin24/OT-M)]![GitHub stars](http://img.shields.io/github/stars/Elin24/OT-M.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DGCC\"\u003e\u003c/a\u003e**[DGCC]** Domain-general Crowd Counting in Unseen Scenarios (**AAAI**)[[paper](https://arxiv.org/abs/2212.02573)] [[code](https://github.com/ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios)]![GitHub stars](http://img.shields.io/github/stars/ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SAFECount\"\u003e\u003c/a\u003e**[SAFECount]** Few-Shot Object Counting With Similarity-Aware Feature Enhancement (**WACV**)[[paper](https://arxiv.org/abs/2201.08959)] [[code](https://github.com/zhiyuanyou/SAFECount)]![GitHub stars](http://img.shields.io/github/stars/zhiyuanyou/SAFECount.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DMCNet\"\u003e\u003c/a\u003e**[DMCNet]** Dynamic Mixture of Counter Network for Location-Agnostic Crowd Counting (**WACV**)[[paper](https://openaccess.thecvf.com/content/WACV2023/papers/Wang_Dynamic_Mixture_of_Counter_Network_for_Location-Agnostic_Crowd_Counting_WACV_2023_paper.pdf)]\n- \u003ca name=\"CACC\"\u003e\u003c/a\u003e**[CACC]** Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation (**ICME**)[[paper](https://arxiv.org/abs/2108.02980v1)]\n- \u003ca name=\"MSSRM\"\u003e\u003c/a\u003e**[MSSRM]** Super-Resolution Information Enhancement For Crowd Counting (**ICASSP**)[[paper](https://arxiv.org/abs/2303.06925)] [[code](https://github.com/PRIS-CV/MSSRM)]![GitHub stars](http://img.shields.io/github/stars/PRIS-CV/MSSRM.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CHS-Net\"\u003e\u003c/a\u003e**[CHS-Net]** Cross-head Supervision for Crowd Counting with Noisy Annotations (**ICASSP**)[[paper](https://arxiv.org/abs/2303.09245)] [[code](https://github.com/RaccoonDML/CHSNet)]![GitHub stars](http://img.shields.io/github/stars/RaccoonDML/CHSNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"Self-ONN\"\u003e\u003c/a\u003e**[Self-ONN]** DroneNet: Crowd Density Estimation using Self-ONNs for Drones (**CCNC**)[[paper](https://arxiv.org/abs/2211.07137)]\n\n### Journal\n- \u003ca name=\"MDC\"\u003e\u003c/a\u003e**[MDC]** Reducing Spatial Labeling Redundancy for Active Semi-supervised Crowd Counting (**T-PAMI**) [[paper](https://arxiv.org/abs/2108.02970)]\n- \u003ca name=\"AGK\"\u003e\u003c/a\u003e**[AGK]** Counting manatee aggregations using deep neural networks and Anisotropic Gaussian Kernel (**Scientific Reports-Nature**) [[paper](https://www.nature.com/articles/s41598-023-45507-3.epdf)] [[code](https://github.com/yeyimilk/deep-learning-for-manatee-counting)]![GitHub stars](http://img.shields.io/github/stars/yeyimilk/deep-learning-for-manatee-counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"GCFL\"\u003e\u003c/a\u003e**[GCFL]** Generalized Characteristic Function Loss for Crowd Analysis in the Frequency Domain (**T-PAMI**) [[paper]()]\n- \u003ca name=\"PESSNet\"\u003e\u003c/a\u003e**[PESSNet]** A Perspective-Embedded Scale-Selection Network for Crowd Counting in Public Transportation (**T-ITS**) [[paper](https://ieeexplore.ieee.org/document/10311097)]\n- \u003ca name=\"MRL\"\u003e\u003c/a\u003e**[MRL]** Semi-Supervised Crowd Counting via Multiple Representation Learning (**TIP**) [[paper](https://ieeexplore.ieee.org/document/10251149)]\n- \u003ca name=\"CDENet\"\u003e\u003c/a\u003e**[CDENet]** Confusion Region Mining for Crowd Counting (**T-NNLS**) [[paper](https://ieeexplore.ieee.org/document/10253468)]\n- \u003ca name=\"FLCC\"\u003e\u003c/a\u003e**[FLCC]** Federated Learning for Crowd Counting in Smart Surveillance Systems (**IEEE IoTJ**) [[paper](https://ieeexplore.ieee.org/document/10221866)]\n- \u003ca name=\"MGANet\"\u003e\u003c/a\u003e**[MGANet]** Crowd Counting Based on Multiscale Spatial Guided Perception Aggregation Network (**T-NNLS**) [[paper](https://ieeexplore.ieee.org/document/10227827)]\n- \u003ca name=\"HMoDE\"\u003e\u003c/a\u003e**[HMoDE]** Redesigning Multi-Scale Neural Network for Crowd Counting (**TIP**) [[paper](https://arxiv.org/abs/2208.02894)][[code](https://github.com/ZPDu/Redesigning-Multi-Scale-Neural-Network-for-Crowd-Counting)]![GitHub stars](http://img.shields.io/github/stars/ZPDu/Redesigning-Multi-Scale-Neural-Network-for-Crowd-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SS-DCNet\"\u003e\u003c/a\u003e**[SS-DCNet]** From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting (**IJCV**) [[paper](https://link.springer.com/article/10.1007/s11263-023-01782-1)](extension of [S-DCNet](#S-DCNet))\n- \u003ca name=\"SSL-FT\"\u003e\u003c/a\u003e**[SSL-FT]** Self-Supervised Learning with Data-Efficient Supervised Fine-Tuning for Crowd Counting (**TMM**) [[paper](https://ieeexplore.ieee.org/document/10057013)]\n- \u003ca name=\"FRVCC\"\u003e\u003c/a\u003e**[FRVCC]** Frame-Recurrent Video Crowd Counting (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/document/10057072)]\n- \u003ca name=\"FLCB\"\u003e\u003c/a\u003e**[FLCB]** Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting (**FITEE**) [[paper](https://link.springer.com/article/10.1631/FITEE.2200380)]\n- \u003ca name=\"MTCP\"\u003e\u003c/a\u003e**[MTCP]** Multi-Task Credible Pseudo-Label Learning for Semi-supervised Crowd Counting (**T-NNLS**) [[paper]()] [[code](https://github.com/ljq2000/MTCP)]![GitHub stars](http://img.shields.io/github/stars/ljq2000/MTCP.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"STGN\"\u003e\u003c/a\u003e**[STGN]** Spatial-Temporal Graph Network for Video Crowd Counting (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/document/9810269)][[code](https://github.com/wuzhe71/STGN)]![GitHub stars](http://img.shields.io/github/stars/wuzhe71/STGN.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"PML_Loss\"\u003e\u003c/a\u003e**[PML_Loss]** Progressive Multi-resolution Loss for Crowd Counting (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/abstract/document/10256036/)][[code](https://github.com/streamer-AP/PML_Loss)]![GitHub stars](http://img.shields.io/github/stars/streamer-AP/PML_Loss.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"EoCo\"\u003e\u003c/a\u003e**[EoCo]** A Unified Object Counting Network with Object Occupation Prior (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/abstract/document/10172099)][[code](https://github.com/Tanyjiang/EOCO)]![GitHub stars](http://img.shields.io/github/stars/Tanyjiang/EOCO.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CmCaF\"\u003e\u003c/a\u003e**[CmCaF]** RGB-D Crowd Counting With Cross-Modal Cycle-Attention Fusion and Fine-Coarse Supervision (**TII**) [[paper](https://ieeexplore.ieee.org/document/9765786)]\n- \u003ca name=\"STC-Crowd\"\u003e\u003c/a\u003e**[STC-Crowd]** Semi-supervised Crowd Counting with Spatial Temporal Consistency and Pseudo-label Filter (**T-CSVT**)[[paper](https://ieeexplore.ieee.org/document/10032602)]\n- \u003ca name=\"LMSFFNet\"\u003e\u003c/a\u003e**[LMSFFNet]** A Lightweight Multiscale Feature Fusion Network for Remote Sensing Object Counting (**TGRS**) [[paper](https://ieeexplore.ieee.org/document/10021616)]\n- \u003ca name=\"DDMD\"\u003e\u003c/a\u003e**[DDMD]** Deformable Density Estimation via Adaptive Representation (**TIP**) [[paper](https://ieeexplore.ieee.org/document/10036469)]\n- \u003ca name=\"UCCF\"\u003e\u003c/a\u003e**[UCCF]** A unified RGB-T crowd counting learning framework (**Image and Vision Computing**) [[arxiv](https://arxiv.org/abs/2202.03843)] [[paper](https://linkinghub.elsevier.com/retrieve/pii/S0262885623000057)]\n- \u003ca name=\"DASECount\"\u003e\u003c/a\u003e**[DASECount]** DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning (**IEEE IOT**) [[paper](https://arxiv.org/abs/2211.10040)]\n- \u003ca name=\"CrowdMLP\"\u003e\u003c/a\u003e**[CrowdMLP]** CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP (**Pattern Recognition**) [[paper](https://arxiv.org/abs/2203.08219)]\n- \u003ca name=\"MTSS\"\u003e\u003c/a\u003e**[MTSS]** Multi-task semi-supervised crowd counting via global to local self-correction (**Pattern Recognition**) [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320323002066)]\n\n### 2022\n### Conference\n- \u003ca name=\"CTFNet\"\u003e\u003c/a\u003e**[CTFNet]** Faster, Lighter, Robuster: A Weakly-Supervised Crowd Analysis Enhancement Network and A Generic Feature Extraction Framework (**CVPR**)[[paper](https://openaccess.thecvf.com/content/CVPR2022W/L3D-IVU/html/Wu_Faster_Lighter_Robuster_A_Weakly-Supervised_Crowd_Analysis_Enhancement_Network_and_CVPRW_2022_paper.html)]\n- \u003ca name=\"CSS-CCNN\"\u003e\u003c/a\u003e**[CSS-CCNN]** Completely Self-Supervised Crowd Counting via Distribution Matching (**ECCV**) [[paper](https://arxiv.org/abs/2009.06420)][[code](https://github.com/val-iisc/css-ccnn)]![GitHub stars](http://img.shields.io/github/stars/val-iisc/css-ccnn.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"TSFADet\"\u003e\u003c/a\u003e**[TSFADet]** Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection (**ECCV**) [[paper](https://arxiv.org/abs/2209.13801)]\n- \u003ca name=\"CSCA\"\u003e\u003c/a\u003e**[CSCA]** Spatio-channel Attention Blocks for Cross-modal Crowd Counting (**ACCV**) [[paper](https://arxiv.org/abs/2210.10392)] [[code](https://github.com/VCLLab/CSCA)]![GitHub stars](http://img.shields.io/github/stars/VCLLab/CSCA.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CUT\"\u003e\u003c/a\u003e**[CUT]** Segmentation Assisted U-shaped Multi-scale Transformer for Crowd Counting (**BMVC**) [[paper](https://www.researchgate.net/publication/364030579_Segmentation_Assisted_U-shaped_Multi-scale_Transformer_for_Crowd_Counting)]\n- \u003ca name=\"MSDTrans\"\u003e\u003c/a\u003e**[MSDTrans]** RGB-T Multi-Modal Crowd Counting Based on Transformer (**BMVC**)[[paper](https://arxiv.org/abs/2301.03033)] [[code](https://github.com/liuzywen/RGBTCC)]![GitHub stars](http://img.shields.io/github/stars/liuzywen/RGBTCC.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"LoViTCrowd\"\u003e\u003c/a\u003e**[LoViTCrowd]** Improving Local Features with Relevant Spatial Information by Vision Transformer for Crowd Counting (**BMVC**) [[paper](https://bmvc2022.mpi-inf.mpg.de/0729.pdf)] [[code](https://github.com/nguyen1312/LoViTCrowd)]![GitHub stars](http://img.shields.io/github/stars/nguyen1312/LoViTCrowd.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SPDCN\"\u003e\u003c/a\u003e**[SPDCN]** Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting (**BMVC**) [[paper](https://bmvc2022.mpi-inf.mpg.de/0313.pdf)]\n- \u003ca name=\"PAP\"\u003e\u003c/a\u003e**[PAP]** Harnessing Perceptual Adversarial Patches for Crowd Counting (**ACM CCS**) [[paper](https://arxiv.org/abs/2109.07986)] [[code](https://github.com/shunchang-liu/PAP-Pytorch)]![GitHub stars](http://img.shields.io/github/stars/shunchang-liu/PAP-Pytorch.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CLTR\"\u003e\u003c/a\u003e**[CLTR]** An End-to-End Transformer Model for Crowd Localization (**ECCV**) [[paper](https://arxiv.org/abs/2202.13065)] [[code](https://github.com/dk-liang/CLTR)]![GitHub stars](http://img.shields.io/github/stars/dk-liang/CLTR.svg?logo=github\u0026label=Stars)[[project](https://dk-liang.github.io/CLTR/)]\n- \u003ca name=\"CF-MVCC\"\u003e\u003c/a\u003e**[CF-MVCC]** Calibration-free Multi-view Crowd Counting (**ECCV**) [[paper](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136690224.pdf)]\n- \u003ca name=\"DC\"\u003e\u003c/a\u003e**[DC]** Discrete-Constrained Regression for Local Counting Models (**ECCV**) [[paper](https://arxiv.org/abs/2207.09865)]\n- \u003ca name=\"DMBA\"\u003e\u003c/a\u003e**[DMBA]** Backdoor Attacks on Crowd Counting (**ACM MM**) [[paper](https://arxiv.org/abs/2205.11398)][[code](https://github.com/Nathangitlab/Backdoor-Attacks-on-Crowd-Counting)]![GitHub stars](http://img.shields.io/github/stars/Nathangitlab/Backdoor-Attacks-on-Crowd-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DACount\"\u003e\u003c/a\u003e**[DACount]** Semi-supervised-Crowd-Counting-via-Density-Agency (**ACM MM**) [[paper](https://arxiv.org/abs/2209.02955)][[code](https://github.com/LoraLinH/Semi-supervised-Crowd-Counting-via-Density-Agency)]![GitHub stars](http://img.shields.io/github/stars/LoraLinH/Semi-supervised-Crowd-Counting-via-Density-Agency.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"ChfL\"\u003e\u003c/a\u003e**[ChfL]** Crowd Counting in the Frequency Domain (**CVPR**) [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Shu_Crowd_Counting_in_the_Frequency_Domain_CVPR_2022_paper.pdf)][[code](https://github.com/wbshu/Crowd_Counting_in_the_Frequency_Domain)]![GitHub stars](http://img.shields.io/github/stars/wbshu/Crowd_Counting_in_the_Frequency_Domain.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"GauNet\"\u003e\u003c/a\u003e**[GauNet]** Rethinking Spatial Invariance of Convolutional Networks for Object Counting (**CVPR**) [[paper](https://arxiv.org/abs/2206.05253)][[code](https://github.com/zhiqic/Rethinking-Counting)]![GitHub stars](http://img.shields.io/github/stars/zhiqic/Rethinking-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DR.VIC\"\u003e\u003c/a\u003e**[DR.VIC]** DR.VIC: Decomposition and Reasoning for Video Individual Counting (**CVPR**) [[paper](https://crabwq.github.io/pdf/2022%20DR.VIC.pdf)][[code](https://github.com/taohan10200/DRNet)]![GitHub stars](http://img.shields.io/github/stars/taohan10200/DRNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CDCC\"\u003e\u003c/a\u003e**[CDCC]** Leveraging Self-Supervision for Cross-Domain Crowd Counting (**CVPR**) [[paper](https://arxiv.org/abs/2103.16291)][[code](https://github.com/weizheliu/Cross-Domain-Crowd-Counting)]![GitHub stars](http://img.shields.io/github/stars/weizheliu/Cross-Domain-Crowd-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"MAN\"\u003e\u003c/a\u003e**[MAN]** Boosting Crowd Counting via Multifaceted Attention (**CVPR**) [[paper](https://arxiv.org/abs/2203.02636)][[code](https://github.com/LoraLinH/Boosting-Crowd-Counting-via-Multifaceted-Attention)]![GitHub stars](http://img.shields.io/github/stars/LoraLinH/Boosting-Crowd-Counting-via-Multifaceted-Attention.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"BLA\"\u003e\u003c/a\u003e**[BLA]** Bi-level Alignment for Cross-Domain Crowd Counting (**CVPR**) [[paper](https://arxiv.org/abs/2205.05844)][[code](https://github.com/Yankeegsj/BLA)]![GitHub stars](http://img.shields.io/github/stars/Yankeegsj/BLA.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"BMNet\"\u003e\u003c/a\u003e**[BMNet]** Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting (**CVPR**)[[paper](https://arxiv.org/abs/2203.08354)][[code](https://github.com/flyinglynx/Bilinear-Matching-Network)]![GitHub stars](http://img.shields.io/github/stars/flyinglynx/Bilinear-Matching-Network.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"\"\u003e\u003c/a\u003e Fine-Grained Counting with Crowd-Sourced Supervision (**CVPRW**) [[paper](https://arxiv.org/abs/2205.11398)]\n- \u003ca name=\"CrowdFormer\"\u003e\u003c/a\u003e**[CrowdFormer]** CrowdFormer: An Overlap Patching Vision Transformer for Top-Down Crowd Counting (**IJCAI**)[[paper](https://www.ijcai.org/proceedings/2022/0215.pdf)]\n- \u003ca name=\"WSCNN\"\u003e\u003c/a\u003e**[WSCNN]** Single Image Object Counting and Localizing using Active-Learning (**WACV**) [[paper](https://openaccess.thecvf.com/content/WACV2022/papers/Huberman-Spiegelglas_Single_Image_Object_Counting_and_Localizing_Using_Active-Learning_WACV_2022_paper.pdf)]\n- \u003ca name=\"IS-Count\"\u003e\u003c/a\u003e**[IS-Count]** IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling (**AAAI**) [[paper](https://arxiv.org/abs/2112.09126)][[code](https://github.com/sustainlab-group/IS-Count)]![GitHub stars](http://img.shields.io/github/stars/sustainlab-group/IS-Count.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"STAN\"\u003e\u003c/a\u003e**[STAN]** A Spatio-Temporal Attentive Network for Video-Based Crowd Counting (**ISCC**) [[paper](https://arxiv.org/abs/2208.11339)]\n- \u003ca name=\"LARL\"\u003e\u003c/a\u003e**[LARL]** Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar (**ICASSP**) [[paper](https://arxiv.org/abs/2110.05876v2)]\n- \u003ca name=\"ESA-Net\"\u003e\u003c/a\u003e**[ESA-Net]** Enhancing and Dissecting Crowd Counting By Synthetic Data (**ICASSP**) [[paper](https://arxiv.org/abs/2201.08992)]\n- \u003ca name=\"MPS\"\u003e\u003c/a\u003e**[MPS]** Multiscale Crowd Counting and Localization By Multitask Point Supervision (**ICASSP**) [[paper](https://arxiv.org/abs/2202.09942)][[code](https://github.com/RCVLab-AiimLab/crowd_counting)]![GitHub stars](http://img.shields.io/github/stars/RCVLab-AiimLab/crowd_counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"TAFNet\"\u003e\u003c/a\u003e**[TAFNet]** TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting (**ISCAS**) [[paper](https://arxiv.org/abs/2202.08517)][[code](https://github.com/TANGHAIHAN/TAFNet)]![GitHub stars](http://img.shields.io/github/stars/TANGHAIHAN/TAFNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"HDNet\"\u003e\u003c/a\u003e**[HDNet]** HDNet: A Hierarchically Decoupled Network for Crowd Counting (**ICME**) [[paper](https://arxiv.org/abs/2212.05722)]\n- \u003ca name=\"SSDA\"\u003e\u003c/a\u003e**[SSDA]** Self-supervised Domain Adaptation in Crowd Counting (**ICIP**) [[paper](https://arxiv.org/abs/2206.03431)]\n- \u003ca name=\"FusionCount\"\u003e\u003c/a\u003e**[FusionCount]** FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion (**ICIP**) [[paper](https://arxiv.org/abs/2202.13660)][[code](https://github.com/YimingMa/FusionCount)]![GitHub stars](http://img.shields.io/github/stars/YimingMa/FusionCount.svg?logo=github\u0026label=Stars)\n\n### Journal\n- \u003ca name=\"PSGCNet\"\u003e\u003c/a\u003e **[PSGCNet]** PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images (**TGRS**) [[paper](https://ieeexplore.ieee.org/abstract/document/9720990)][[code](https://github.com/gaoguangshuai/psgcnet)]![GitHub stars](http://img.shields.io/github/stars/gaoguangshuai/psgcnet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"MVMS\"\u003e\u003c/a\u003e**[MVMS]** Wide-Area Crowd Counting: Multi-View Fusion Networks for Counting in Large Scenes (**IJCV**) [[paper](https://arxiv.org/abs/2012.00946)](extension of [MVMS](#MVMS))\n- \u003ca name=\"DEFNet\"\u003e\u003c/a\u003e**[DEFNet]** DEFNet: Dual-Branch Enhanced Feature Fusion Network for RGB-T Crowd Counting (**TITS**) [[paper](https://ieeexplore.ieee.org/abstract/document/9889192)][[code](https://github.com/panyi95/DEFNet)]![GitHub stars](http://img.shields.io/github/stars/panyi95/DEFNet.svg?logo=github\u0026label=Stars) \n- \u003ca name=\"CLRNet\"\u003e\u003c/a\u003e**[CLRNet]** CLRNet: A Cross Locality Relation Network for Crowd Counting in Videos (**T-NNLS**) [[paper](https://ieeexplore.ieee.org/document/9913683)]\n- \u003ca name=\"AGCCM\"\u003e\u003c/a\u003e**[AGCCM]** Attention-guided Collaborative Counting (**TIP**) [[paper](https://ieeexplore.ieee.org/document/9906560)]\n- \u003ca name=\"GNA\"\u003e\u003c/a\u003e**[GNA]** Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark (**TIP**) [[paper](https://ieeexplore.ieee.org/abstract/document/9893023/)][[code](https://github.com/HopLee6/VSCrowd-Dataset)]![GitHub stars](http://img.shields.io/github/stars/HopLee6/VSCrowd-Dataset.svg?logo=github\u0026label=Stars) \n- \u003ca name=\"LibraNet+DQN\"\u003e\u003c/a\u003e **[LibraNet+DQN]** Counting Crowd by Weighing Counts: A Sequential Decision-Making Perspective (**T-NNLS**) [[paper](https://ieeexplore.ieee.org/document/9887967/)][[code](https://git.io/libranet)](extension of [LibraNet](#LibraNet))\n- \u003ca name=\"FIDTM\"\u003e\u003c/a\u003e**[FIDTM]** Focal Inverse Distance Transform Maps for Crowd Localization (**TMM**)[[paper](https://ieeexplore.ieee.org/document/9875106)] [[code](https://github.com/dk-liang/FIDTM)]![GitHub stars](http://img.shields.io/github/stars/dk-liang/FIDTM.svg?logo=github\u0026label=Stars) [[project](https://dk-liang.github.io/FIDTM/)]\n- \u003ca name=\"NDConv\"\u003e\u003c/a\u003e**[NDConv]** An Improved Normed-Deformable Convolution for Crowd Counting (**SPL**) [[paper](https://arxiv.org/abs/2206.08084)]\n- \u003ca name=\"RAN\"\u003e\u003c/a\u003e**[RAN]** Region-Aware Network: Model Human’s Top-Down Visual Perception Mechanism for Crowd Counting (**Neural Networks**) [[paper](https://arxiv.org/abs/2106.12163)]\n- \u003ca name=\"HANet\"\u003e\u003c/a\u003e**[HANet]** Hybrid attention network based on progressive embedding scale-context for crowd counting (**Information Sciences**) [[paper](https://arxiv.org/abs/2106.02324)]\n- \u003ca name=\"TransCrowd\"\u003e\u003c/a\u003e**[TransCrowd]** TransCrowd: Weakly-Supervised Crowd Counting with Transformer (**Science China Information Sciences**) [[paper](https://arxiv.org/abs/2104.09116)] [[code](https://github.com/dk-liang/TransCrowd)]![GitHub stars](http://img.shields.io/github/stars/dk-liang/TransCrowd.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"STNet\"\u003e\u003c/a\u003e**[STNet]** STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting (**TMM**) [[paper](https://ieeexplore.ieee.org/document/9681311)]\n- \u003ca name=\"SGANet\"\u003e\u003c/a\u003e**[SGANet]** Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss (**TITS**) [[paper](https://ieeexplore.ieee.org/document/9678116)]\n- \u003ca name=\"CTASNet\"\u003e\u003c/a\u003e**[CTASNet]** Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/document/9899467)]\n- \u003ca name=\"SSR-HEF\"\u003e\u003c/a\u003e**[SSR-HEF]** SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing (**TII**) [[paper](https://arxiv.org/abs/2204.07406)]\n- \u003ca name=\"ECCNAS\"\u003e\u003c/a\u003e **[ECCNAS]** \tECCNAS: Efficient Crowd Counting Neural Architecture Search (**TOMM**) [[paper](https://dl.acm.org/doi/abs/10.1145/3465455)]\n- \u003ca name=\"SSCC\"\u003e\u003c/a\u003e **[SSCC]** \tScene-specific crowd counting using synthetic training images (**Pattern Recognition**) [[paper](https://www.sciencedirect.com/science/article/pii/S0031320321006609)]\n- \u003ca name=\"SL-ViT\"\u003e\u003c/a\u003e **[SL-ViT]** Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead (**Neural Networks**) [[paper](https://arxiv.org/abs/2105.09121)]\n- \u003ca name=\"DCST\"\u003e\u003c/a\u003e **[DCST]** Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer (**Neurocomputing**) [[paper](https://arxiv.org/abs/2108.00584)]\n- A survey on deep learning-based single image crowd counting: Network design, loss function and supervisory signal (**Neurocomputing**) [[paper](https://arxiv.org/abs/2012.15685)]\n\n### 2021\n### Conference\n- \u003ca name=\"GNet\"\u003e\u003c/a\u003e**[GNet]** Gaussian map predictions for 3D surface feature localisation and counting (**BMVC**) [[paper](https://www.bmvc2021-virtualconference.com/assets/papers/1417.pdf)]\n- \u003ca name=\"PFSNet\"\u003e\u003c/a\u003e**[PFSNet]** Robust Crowd Counting via Image Enhancement and Dynamic Feature Selection (**BMVC**) [[paper](https://www.bmvc2021-virtualconference.com/assets/papers/1387.pdf)]\n- \u003ca name=\"URC\"\u003e\u003c/a\u003e**[URC]** Crowd Counting With Partial Annotations in an Image (**ICCV**) [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Xu_Crowd_Counting_With_Partial_Annotations_in_an_Image_ICCV_2021_paper.pdf)]\n- \u003ca name=\"MFDC\"\u003e\u003c/a\u003e**[MFDC]** Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network (**ICCV**) [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Exploiting_Sample_Correlation_for_Crowd_Counting_With_Multi-Expert_Network_ICCV_2021_paper.pdf)]\n- \u003ca name=\"SDNet\"\u003e\u003c/a\u003e**[SDNet]** Towards A Universal Model for Cross-Dataset Crowd Counting (**ICCV**) [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Ma_Towards_a_Universal_Model_for_Cross-Dataset_Crowd_Counting_ICCV_2021_paper.pdf)]\n- \u003ca name=\"P2PNet\"\u003e\u003c/a\u003e**[P2PNet]** Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework (**ICCV(Oral)**) [[paper](https://arxiv.org/abs/2107.12746)][[code](https://github.com/TencentYoutuResearch/CrowdCounting-P2PNet)]![GitHub stars](http://img.shields.io/github/stars/TencentYoutuResearch/CrowdCounting-P2PNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"UEPNet\"\u003e\u003c/a\u003e**[UEPNet]** Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting (**ICCV**) [[paper](https://arxiv.org/abs/2107.12619)][[code](https://github.com/TencentYoutuResearch/CrowdCounting-UEPNet)]![GitHub stars](http://img.shields.io/github/stars/TencentYoutuResearch/CrowdCounting-UEPNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SUA\"\u003e\u003c/a\u003e**[SUA]** Spatial Uncertainty-Aware Semi-Supervised Crowd Counting (**ICCV**) [[paper](https://arxiv.org/abs/2107.13271)][[code](https://github.com/smallmax00/SUA_crowd_counting)]![GitHub stars](http://img.shields.io/github/stars/smallmax00/SUA_crowd_counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DKPNet\"\u003e\u003c/a\u003e**[DKPNet]** Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (**ICCV**) [[paper](https://arxiv.org/abs/2108.08023)][[code](https://github.com/Zhaoyi-Yan/DKPNet)]![GitHub stars](http://img.shields.io/github/stars/Zhaoyi-Yan/DKPNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CC-AV\"\u003e\u003c/a\u003e**[CC-AV]** Audio-Visual Transformer Based Crowd Counting (**ICCVW**) [[paper](https://arxiv.org/abs/2109.01926)]\n- \u003ca name=\"BinLoss\"\u003e\u003c/a\u003e**[BinLoss]** Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting (**ACM MM**) [[paper](https://arxiv.org/abs/2108.08784)][[code](https://github.com/atmacvit/bincrowd)]![GitHub stars](http://img.shields.io/github/stars/atmacvit/bincrowd?label=Stars\u0026logo=Github)\n- \u003ca name=\"C2MoT\"\u003e\u003c/a\u003e**[C2MoT]** Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting (**ACM MM**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3474085.3475230)][[code](https://github.com/jimmy-dq/C2MOT)]![GitHub stars](http://img.shields.io/github/stars/jimmy-dq/C2MOT?label=Stars\u0026logo=Github)\n- \u003ca name=\"ASNet\"\u003e\u003c/a\u003e**[ASNet]** Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network (**ACM MM**) [[paper](https://arxiv.org/abs/2107.12858)]\n- \u003ca name=\"APAM\"\u003e\u003c/a\u003e**[APAM]** Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting (**ACM MM**) [[paper](https://arxiv.org/abs/2104.10868)][[code](https://github.com/harrywuhust2022/Adv-Crowd-analysis)]![GitHub stars](http://img.shields.io/github/stars/harrywuhust2022/Adv-Crowd-analysis.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"S3\"\u003e\u003c/a\u003e**[S3]** Direct Measure Matching for Crowd Counting (**IJCAI**) [[paper](https://www.ijcai.org/proceedings/2021/0116.pdf)]\n- \u003ca name=\"BM-Count\"\u003e\u003c/a\u003e**[BM-Count]** Bipartite Matching for Crowd Counting with Point Supervision (**IJCAI**) [[paper](https://www.ijcai.org/proceedings/2021/0119.pdf)]\n- \u003ca name=\"GLoss\"\u003e\u003c/a\u003e**[GLoss]** A Generalized Loss Function for Crowd Counting and Localization (**CVPR**) [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Wan_A_Generalized_Loss_Function_for_Crowd_Counting_and_Localization_CVPR_2021_paper.pdf)]\n- \u003ca name=\"CVCS\"\u003e\u003c/a\u003e**[CVCS]** Cross-View Cross-Scene Multi-View Crowd Counting (**CVPR**) [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Cross-View_Cross-Scene_Multi-View_Crowd_Counting_CVPR_2021_paper.pdf)]\n- \u003ca name=\"STANet\"\u003e\u003c/a\u003e **[STANet]** Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark (**CVPR**) [[paper](https://arxiv.org/abs/2105.02440)][[code](https://github.com/VisDrone/DroneCrowd)]![GitHub stars](http://img.shields.io/github/stars/VisDrone/DroneCrowd.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"RGBT-CC\"\u003e\u003c/a\u003e **[RGBT-CC]** Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting (**CVPR**) [[paper](https://arxiv.org/abs/2012.04529)][[code](https://github.com/chen-judge/RGBTCrowdCounting)]![GitHub stars](http://img.shields.io/github/stars/chen-judge/RGBTCrowdCounting.svg?logo=github\u0026label=Stars)[[Project](http://lingboliu.com/RGBT_Crowd_Counting.html#)]\n- \u003ca name=\"EDIREC-Net\"\u003e\u003c/a\u003e **[EDIREC-Net]** Error-Aware Density Isomorphism Reconstruction for Unsupervised Cross-Domain Crowd Counting (**AAAI**) [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/16245)][[code](https://github.com/GehenHe/EDIREC-Net)]![GitHub stars](http://img.shields.io/github/stars/GehenHe/EDIREC-Net.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SASNet\"\u003e\u003c/a\u003e **[SASNet]** To Choose or to Fuse? Scale Selection for Crowd Counting (**AAAI**) [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/16360)][[code](https://github.com/TencentYoutuResearch/CrowdCounting-SASNet)]![GitHub stars](http://img.shields.io/github/stars/TencentYoutuResearch/CrowdCounting-SASNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"UOT\"\u003e\u003c/a\u003e **[UOT]** Learning to Count via Unbalanced Optimal Transport (**AAAI**) [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/16332)]\n- \u003ca name=\"TopoCount\"\u003e\u003c/a\u003e **[TopoCount]**  Localization in the Crowd with Topological Constraints (**AAAI**) [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/16170)][[code](https://github.com/ShahiraAbousamra/TopoCount)]![GitHub stars](http://img.shields.io/github/stars/ShahiraAbousamra/TopoCount.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CFANet\"\u003e\u003c/a\u003e **[CFANet]** Coarse- and Fine-grained Attention Network with Background-aware Loss for Crowd Density Map Estimation (**WACV**) [[paper](https://arxiv.org/abs/2011.03721)][[code](https://github.com/rongliangzi/MARUNet)]![GitHub stars](http://img.shields.io/github/stars/rongliangzi/MARUNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"BSCC\"\u003e\u003c/a\u003e **[BSCC]** Understanding the impact of mistakes on background regions in crowd counting (**WACV**) [[paper](https://arxiv.org/abs/2003.13759)]\n- \u003ca name=\"CFOCNet\"\u003e\u003c/a\u003e **[CFOCNet]** Class-agnostic Few-shot Object Counting (**WACV**) [[paper](https://winstonhsu.info/wp-content/uploads/2020/11/yang21class-agnostic.pdf)][[code](https://github.com/SinicaGroup/Class-agnostic-Few-shot-Object-Counting)]![GitHub stars](http://img.shields.io/github/stars/SinicaGroup/Class-agnostic-Few-shot-Object-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SCALNet\"\u003e\u003c/a\u003e **[SCALNet]** Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization (**ICMEW**) [[paper](https://arxiv.org/abs/2104.12505)][[code](https://github.com/WangyiNTU/SCALNet)]![GitHub stars](http://img.shields.io/github/stars/WangyiNTU/SCALNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DSNet\"\u003e\u003c/a\u003e **[DSNet]** Dense Scale Network for Crowd Counting (**ICMR**) [[paper](https://arxiv.org/abs/1906.09707)][unofficial code: [PyTorch](https://github.com/rongliangzi/Dense-Scale-Network-for-Crowd-Counting)]![GitHub stars](http://img.shields.io/github/stars/rongliangzi/Dense-Scale-Network-for-Crowd-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"FCVF\"\u003e\u003c/a\u003e **[FCVF]** Learning Factorized Cross-View Fusion for Multi-View Crowd Counting (**ICME**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9428284)]\n- \u003ca name=\"IDK\"\u003e\u003c/a\u003e **[IDK]** Leveraging Intra-Domain Knowledge to Strengthen Cross-Domain Crowd Counting (**ICME**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9428159)]\n- \u003ca name=\"CRANet\"\u003e\u003c/a\u003e **[CRANet]** CRANet: Cascade Residual Attention Network for Crowd Counting (**ICME**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9428236)]\n\n### Journal\n- \u003ca name=\"DPDNet\"\u003e\u003c/a\u003e**[DPDNet]** Locating and Counting Heads in Crowds With a Depth Prior (**T-PAMI**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9601215)] [[code](https://github.com/svip-lab/Locating_Counting_with_a_Depth_Prior)]![GitHub stars](http://img.shields.io/github/stars/svip-lab/Locating_Counting_with_a_Depth_Prior.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"EPF\"\u003e\u003c/a\u003e**[EPF]** Counting People by Estimating People Flows (**TPAMI**) [[paper](https://arxiv.org/abs/2012.00452)][[code](https://github.com/weizheliu/People-Flows)]![GitHub stars](http://img.shields.io/github/stars/weizheliu/People-Flows.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"LA-Batch\"\u003e\u003c/a\u003e**[LA-Batch]** Locality-Aware Crowd Counting (**TPAMI**) [[paper](https://www.computer.org/csdl/journal/tp/5555/01/09346018/1qV39sNsjWU)]\n- \u003ca name=\"AutoScale\"\u003e\u003c/a\u003e**[AutoScale]** AutoScale: Learning to Scale for Crowd Counting (**IJCV**) [[paper](https://link.springer.com/article/10.1007/s11263-021-01542-z)] (extension of [L2SM](#L2SM))[[code](https://github.com/dk-liang/AutoScale)]![GitHub stars](http://img.shields.io/github/stars/dk-liang/AutoScale.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DSACA\"\u003e\u003c/a\u003e**[DSACA]** Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting  (**SPL**) [[paper](https://ieeexplore.ieee.org/abstract/document/9479708)] [[code](https://github.com/PRIS-CV/DSACA)]![GitHub stars](http://img.shields.io/github/stars/PRIS-CV/DSACA.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"NLT\"\u003e\u003c/a\u003e **[NLT]** Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting (**T-NNLS**) [[paper](https://arxiv.org/abs/2004.02133)] [[code]](https://github.com/taohan10200/NLT)]![GitHub stars](http://img.shields.io/github/stars/taohan10200/NLT.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DACC\"\u003e\u003c/a\u003e **[DACC]** Domain-Adaptive Crowd Counting via High-Quality Image Translation and Density Reconstruction (**T-NNLS**) [[paper](https://arxiv.org/abs/1912.03677)]\n- \u003ca name=\"MATT\"\u003e\u003c/a\u003e **[MATT]** Towards Using Count-level Weak Supervision for Crowd Counting (**Pattern Recognition**) [[paper](https://arxiv.org/abs/2003.00164)]\n- \u003ca name=\"D2C\"\u003e\u003c/a\u003e **[D2C]** Decoupled Two-Stage Crowd Counting and Beyond (**TIP**) [[paper](https://ieeexplore.ieee.org/document/9347700)][[code](https://github.com/hustaia/Decoupled_Two-Stage_Counting)]![GitHub stars](http://img.shields.io/github/stars/hustaia/Decoupled_Two-stage_Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"TBC\"\u003e\u003c/a\u003e **[TBC]** Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets (**TIP**) [[paper](https://ieeexplore.ieee.org/document/9298464)]\n- \u003ca name=\"FGCC\"\u003e\u003c/a\u003e **[FGCC]** Fine-Grained Crowd Counting (**TIP**) [[paper](https://arxiv.org/abs/2007.06146)]\n- \u003ca name=\"PSODC\"\u003e\u003c/a\u003e **[PSODC]** A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds (**TIP**) [[paper](https://arxiv.org/abs/2007.12831)][[code](https://github.com/WangyiNTU/Point-supervised-crowd-detection)]![GitHub stars](http://img.shields.io/github/stars/WangyiNTU/Point-supervised-crowd-detection.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"EPA\"\u003e\u003c/a\u003e **[EPA]** Embedding Perspective Analysis Into Multi-Column Convolutional Neural Network for Crowd Counting (**TIP**) [[paper](https://ieeexplore.ieee.org/document/9293174)]\n- \u003ca name=\"PFDNet\"\u003e\u003c/a\u003e**[PFDNet]** Crowd Counting via Perspective-Guided Fractional-Dilation Convolution (**TMM**) [[paper](https://ieeexplore.ieee.org/document/9468694)](extension of [PGCNet](#PGCNet))\n- \u003ca name=\"STDNet\"\u003e\u003c/a\u003e **[STDNet]** Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd Estimation (**TMM**) [[paper](https://arxiv.org/abs/2101.12439)]\n- \u003ca name=\"AdaCrowd\"\u003e\u003c/a\u003e **[AdaCrowd]** AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting (**TMM**) [[paper](https://arxiv.org/abs/2010.12141)][[code](https://github.com/maheshkkumar/adacrowd)]![GitHub stars](http://img.shields.io/github/stars/maheshkkumar/adacrowd.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DCANet\"\u003e\u003c/a\u003e**[DCANet]** Towards Learning Multi-domain Crowd Counting (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9658506)] [[code](https://github.com/Zhaoyi-Yan/DCANet)]![GitHub stars](http://img.shields.io/github/stars/Zhaoyi-Yan/DCANet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"PDANet\"\u003e\u003c/a\u003e **[PDANet]** PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting (**Neurocomputing**) [[paper](https://arxiv.org/abs/2001.05643)]\n- \u003ca name=\"ScSiNet\"\u003e\u003c/a\u003e **[ScSiNet]** Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting (**Neurocomputing**) [[paper](https://arxiv.org/abs/2005.11943)]\n- \u003ca name=\"PRM\"\u003e\u003c/a\u003e **[PRM]** Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network (**Neurocomputing**) [[paper](https://arxiv.org/abs/2112.09664)]\n- \u003ca name=\"DeepCorn\"\u003e\u003c/a\u003e **[DeepCorn]** DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation (**Knowledge-Based Systems**) [[paper](https://arxiv.org/abs/2007.10521)]\n\n### 2020\n### Conference\n- \u003ca name=\"DM-Count\"\u003e\u003c/a\u003e **[DM-Count]** Distribution Matching for Crowd Counting (**NeurIPS**) [[paper](https://arxiv.org/abs/2009.13077)][[code](https://github.com/cvlab-stonybrook/DM-Count)]![GitHub stars](http://img.shields.io/github/stars/cvlab-stonybrook/DM-Count.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"MNA\"\u003e\u003c/a\u003e **[MNA]** Modeling Noisy Annotations for Crowd Counting (**NeurIPS**) [[paper](http://visal.cs.cityu.edu.hk/static/pubs/conf/nips2020-noisycc-web.pdf)]\n- \u003ca name=\"SKT\"\u003e\u003c/a\u003e **[SKT]** Efficient Crowd Counting via Structured Knowledge Transfer (**ACM MM(oral)**) [[paper](https://arxiv.org/abs/2003.10120)][[code](https://github.com/HCPLab-SYSU/SKT)]![GitHub stars](http://img.shields.io/github/stars/HCPLab-SYSU/SKT.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DPN\"\u003e\u003c/a\u003e **[DPN]** Learning Scales from Points: A Scale-aware Probabilistic Model for Crowd Counting (**ACM MM(oral)**) [[paper](https://dl.acm.org/doi/10.1145/3394171.3413642)]\n- \u003ca name=\"RDBT\"\u003e\u003c/a\u003e **[RDBT]** Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer (**ACM MM**) [[paper](https://arxiv.org/abs/2008.05383)]\n- \u003ca name=\"VisDrone-CC2020\"\u003e\u003c/a\u003e **[VisDrone-CC2020]** VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results (**ECCV**) [[paper](https://arxiv.org/abs/2107.08766)]\n- \u003ca name=\"EPF\"\u003e\u003c/a\u003e **[EPF]** Estimating People Flows to Better Count Them in Crowded Scenes (**ECCV**) [[paper](https://arxiv.org/abs/1911.10782)][[code](https://github.com/weizheliu/People-Flows)]![GitHub stars](http://img.shields.io/github/stars/weizheliu/People-Flows.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"AMSNet\"\u003e\u003c/a\u003e **[AMSNet]** NAS-Count: Counting-by-Density with Neural Architecture Search (**ECCV**) [[paper](https://arxiv.org/abs/2003.00217)]\n- \u003ca name=\"AMRNet\"\u003e\u003c/a\u003e **[AMRNet]** Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting (**ECCV**) [[paper](https://arxiv.org/abs/2005.05776)][[code](https://github.com/xiyang1012/Local-Crowd-Counting)]![GitHub stars](http://img.shields.io/github/stars/xiyang1012/Local-Crowd-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"LibraNet\"\u003e\u003c/a\u003e **[LibraNet]** Weighting Counts: Sequential Crowd Counting by Reinforcement Learning (**ECCV**) [[paper](https://arxiv.org/abs/2007.08260)][[code](https://github.com/poppinace/libranet)]![GitHub stars](http://img.shields.io/github/stars/poppinace/libranet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"GP\"\u003e\u003c/a\u003e **[GP]** Learning to Count in the Crowd from Limited Labeled Data (**ECCV**) [[paper](https://arxiv.org/abs/2007.03195)]\n- \u003ca name=\"IRAST\"\u003e\u003c/a\u003e **[IRAST]** Semi-supervised Crowd Counting via Self-training on Surrogate Tasks (**ECCV**) [[paper](https://arxiv.org/abs/2007.03207)]\n- \u003ca name=\"PSSW\"\u003e\u003c/a\u003e **[PSSW]** Active Crowd Counting with Limited Supervision (**ECCV**) [[paper](https://arxiv.org/abs/2007.06334)]\n- \u003ca name=\"CCLS\"\u003e\u003c/a\u003e **[CCLS]** Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations (**ECCV**) [[paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530001.pdf)]\n- \u003ca name=\"Bi-pathNet\"\u003e\u003c/a\u003e **[Bi-pathNet]** A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View (**ECCVW**) [[paper](https://arxiv.org/abs/2009.13723)]\n- \u003ca name=\"ADSCNet\"\u003e\u003c/a\u003e **[ADSCNet]** Adaptive Dilated Network with Self-Correction Supervision for Counting (**CVPR**) [[paper](http://openaccess.thecvf.com/content_CVPR_2020/papers/Bai_Adaptive_Dilated_Network_With_Self-Correction_Supervision_for_Counting_CVPR_2020_paper.pdf)] \n- \u003ca name=\"RPNet\"\u003e\u003c/a\u003e **[RPNet]** Reverse Perspective Network for Perspective-Aware Object Counting (**CVPR**) [[paper](http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Reverse_Perspective_Network_for_Perspective-Aware_Object_Counting_CVPR_2020_paper.pdf)] [[code](https://github.com/CrowdCounting)]\n- \u003ca name=\"ASNet\"\u003e\u003c/a\u003e **[ASNet]** Attention Scaling for Crowd Counting (**CVPR**) [[paper](http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Attention_Scaling_for_Crowd_Counting_CVPR_2020_paper.pdf)] [[code](https://github.com/laridzhang/ASNet)]![GitHub stars](http://img.shields.io/github/stars/laridzhang/ASNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SRF-Net\"\u003e\u003c/a\u003e **[SRF-Net]** Scale-Aware Rolling Fusion Network for Crowd Counting (**ICME**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9102854)]\n- \u003ca name=\"EDC\"\u003e\u003c/a\u003e **[EDC]** Learning Error-Driven Curriculum for Crowd Counting (**ICPR**) [[paper](https://arxiv.org/pdf/2007.09676.pdf)][[code](https://github.com/FDU-VTS/TutorNet_Crowd_Counting)]![GitHub stars](http://img.shields.io/github/stars/FDU-VTS/TutorNet_Crowd_Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"PRM\"\u003e\u003c/a\u003e **[PRM]** Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting (**ICPR**) [[paper](https://arxiv.org/abs/2010.01664)]\n- \u003ca name=\"M-SFANet\"\u003e\u003c/a\u003e **[M-SFANet]** Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting (**ICPR**) [[paper](https://arxiv.org/abs/2003.05586)][[code](https://github.com/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting)]![GitHub stars](http://img.shields.io/github/stars/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"HSRNet\"\u003e\u003c/a\u003e **[HSRNet]** Crowd Counting via Hierarchical Scale Recalibration Network (**ECAI**) [[paper](https://arxiv.org/abs/2003.03545)]\n- \u003ca name=\"DeepCount\"\u003e\u003c/a\u003e **[DeepCount]** Deep Density-aware Count Regressor (**ECAI**) [[paper](https://arxiv.org/abs/1908.03314)][[code](https://github.com/GeorgeChenZJ/deepcount)]![GitHub stars](http://img.shields.io/github/stars/GeorgeChenZJ/deepcount.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SOFA-Net\"\u003e\u003c/a\u003e **[SOFA-Net]** SOFA-Net: Second-Order and First-order Attention Network for Crowd Counting (**BMVC**) [[paper](https://arxiv.org/abs/2008.03723)]\n- \u003ca name=\"CWAN\"\u003e\u003c/a\u003e **[CWAN]** Weakly Supervised Crowd-Wise Attention For Robust Crowd Counting (**ICASSP**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9054258)]\n- \u003ca name=\"AGRD\"\u003e\u003c/a\u003e **[AGRD]** Attention Guided Region Division for Crowd Counting (**ICASSP**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9053761)]\n- \u003ca name=\"BBA-NET\"\u003e\u003c/a\u003e **[BBA-NET]** BBA-NET: A Bi-Branch Attention Network For Crowd Counting (**ICASSP**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9053955)]\n- \u003ca name=\"SMANet\"\u003e\u003c/a\u003e **[SMANet]** Stochastic Multi-Scale Aggregation Network for Crowd Counting (**ICASSP**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9054238)]\n- \u003ca name=\"Stacked-Pool\"\u003e\u003c/a\u003e **[Stacked-Pool]** Stacked Pooling For Boosting Scale Invariance Of Crowd Counting (**ICASSP**) [[paper](https://siyuhuang.github.io/papers/ICASSP-2020-STACKED%20POOLING%20FOR%20BOOSTING%20SCALE%20INVARIANCE%20OF%20CROWD%20COUNTING.pdf)] [[arxiv](https://arxiv.org/abs/1808.07456)] [[code](https://github.com/siyuhuang/crowdcount-stackpool)]![GitHub stars](http://img.shields.io/github/stars/siyuhuang/crowdcount-stackpool.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"MSPNET\"\u003e\u003c/a\u003e **[MSPNET]** Multi-supervised Parallel Network for Crowd Counting (**ICASSP**) [[paper](https://crabwq.github.io/pdf/2020%20MSPNET%20Multi-supervised%20Parallel%20Network%20for%20Crowd%20Counting.pdf)]\n- \u003ca name=\"ASPDNet\"\u003e\u003c/a\u003e **[ASPDNet]** Counting dense objects in remote sensing images (**ICASSP**) [[paper](https://arxiv.org/abs/2002.05928)]\n- \u003ca name=\"FSC\"\u003e\u003c/a\u003e **[FSC]** Focus on Semantic Consistency for Cross-domain Crowd Understanding (**ICASSP**) [[paper](https://arxiv.org/abs/2002.08623)]\n- \u003ca name=\"C-CNN\"\u003e\u003c/a\u003e **[C-CNN]** A Real-Time Deep Network for Crowd Counting (**ICASSP**) [[arxiv](https://arxiv.org/abs/2002.06515)][[ieee](https://ieeexplore.ieee.org/abstract/document/9053780/)]\n- \u003ca name=\"HyGnn\"\u003e\u003c/a\u003e **[HyGnn]** Hybrid  Graph  Neural  Networks  for  Crowd  Counting (**AAAI**) [[paper](https://arxiv.org/abs/2002.00092)]\n- \u003ca name=\"DUBNet\"\u003e\u003c/a\u003e **[DUBNet]** Crowd Counting with Decomposed Uncertainty (**AAAI**) [[paper](https://arxiv.org/abs/1903.07427)]\n- \u003ca name=\"SDANet\"\u003e\u003c/a\u003e **[SDANet]** Shallow  Feature  based  Dense  Attention  Network  for  Crowd  Counting (**AAAI**) [[paper](http://wrap.warwick.ac.uk/130173/1/WRAP-shallow-feature-dense-attention-crowd-counting-Han-2019.pdf)]\n- \u003ca name=\"3DCC\"\u003e\u003c/a\u003e **[3DCC]** 3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels (**AAAI**) [[paper](https://arxiv.org/abs/2003.08162)][[Project](http://visal.cs.cityu.edu.hk/research/aaai20-3d-counting/)]\n- \u003ca name=\"FFSA\"\u003e\u003c/a\u003e **[FSSA]** Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning (**WACV**) [[paper](https://arxiv.org/abs/2002.00264)][[code](https://github.com/maheshkkumar/fscc)] ![GitHub stars](http://img.shields.io/github/stars/maheshkkumar/fscc.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CC-Mod\"\u003e\u003c/a\u003e **[CC-Mod]** Plug-and-Play Rescaling Based Crowd Counting in Static Images (**WACV**) [[paper](https://arxiv.org/abs/2001.01786)]\n- \u003ca name=\"CTN\"\u003e\u003c/a\u003e **[CTN]** Uncertainty Estimation and Sample Selection for Crowd Counting (**ACCV**) [[paper](https://arxiv.org/abs/2009.14411)]\n- \u003ca name=\"ikNN\"\u003e\u003c/a\u003e **[ikNN]** Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling (**VISAPP**) [[paper](https://arxiv.org/abs/1902.05379)]\n\n### Journal\n- \u003ca name=\"NWPU\"\u003e\u003c/a\u003e **[NWPU]** NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization (**T-PAMI**) [[paper](https://arxiv.org/abs/2001.03360)][[code](https://gjy3035.github.io/NWPU-Crowd-Sample-Code/)]![GitHub stars](http://img.shields.io/github/stars/gjy3035/NWPU-Crowd-Sample-Code.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"KDMG\"\u003e\u003c/a\u003e **[KDMG]** Kernel-based Density Map Generation for Dense Object Counting (**T-PAMI**) [[paper](https://ieeexplore.ieee.org/document/9189836)][[code](https://github.com/jia-wan/KDMG_Counting)]![GitHub stars](http://img.shields.io/github/stars/jia-wan/KDMG_Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"JHU-CROWD\"\u003e\u003c/a\u003e **[JHU-CROWD]** JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (**T-PAMI**) [[paper](https://arxiv.org/abs/2004.03597)](extension of [CG-DRCN](#CG-DRCN))\n- \u003ca name=\"LSC-CNN\"\u003e\u003c/a\u003e **[LSC-CNN]** Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection (**T-PAMI**) [[paper](https://arxiv.org/abs/1906.07538)][[code](https://github.com/val-iisc/lsc-cnn)]![GitHub stars](http://img.shields.io/github/stars/val-iisc/lsc-cnn.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"PWCU\"\u003e\u003c/a\u003e **[PWCU]** Pixel-wise Crowd Understanding via Synthetic Data (**IJCV**) [[paper](https://arxiv.org/abs/2007.16032)]![GitHub stars](http://img.shields.io/github/stars/gjy3035/GCC-SFCN.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CRNet\"\u003e\u003c/a\u003e **[CRNet]** Crowd Counting via Cross-stage Refinement Networks (**TIP**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9096602)][[code](https://github.com/lytgftyf/Crowd-Counting-via-Cross-stage-Refinement-Networks)] ![GitHub stars](http://img.shields.io/github/stars/lytgftyf/Crowd-Counting-via-Cross-stage-Refinement-Networks.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"BNFDD\"\u003e\u003c/a\u003e **[BNFDD]** Background Noise Filtering and Distribution Dividing for Crowd Counting (**TIP**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9161353)]\n- \u003ca name=\"FADA\"\u003e\u003c/a\u003e **[FADA]** Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance (**TCYB**) [[paper](https://arxiv.org/abs/1912.03672)]\n- \u003ca name=\"MS-GAN\"\u003e\u003c/a\u003e **[MS-GAN]** Adversarial Learning for Multiscale Crowd Counting Under Complex Scenes (**TCYB**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8949751)]\n- \u003ca name=\"DCL\"\u003e\u003c/a\u003e **[DCL]** Density-aware Curriculum Learning for Crowd Counting (**TCYB**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9275392)][[code](https://github.com/Elin24/DCL-CrowdCounting)]![GitHub stars](http://img.shields.io/github/stars/Elin24/DCL-CrowdCounting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"ZoomCount\"\u003e\u003c/a\u003e **[ZoomCount]** ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images (**T-CSVT**) [[paper](https://arxiv.org/abs/2002.12256)]\n- \u003ca name=\"DensityCNN\"\u003e\u003c/a\u003e **[DensityCNN]** Density-Aware Multi-Task Learning for Crowd Counting (**TMM**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9037113)]\n- \u003ca name=\"DENet\"\u003e\u003c/a\u003e **[DENet]** DENet: A Universal Network for Counting Crowd with Varying Densities and Scales (**TMM**) [[paper](https://arxiv.org/abs/1904.08056)][[code](https://github.com/liuleiBUAA/DENet)]![GitHub stars](http://img.shields.io/github/stars/liuleiBUAA/DENet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CLPNet\"\u003e\u003c/a\u003e **[CLPNet]** Cross-Level Parallel Network for Crowd Counting (**TII**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8798674)]\n- \u003ca name=\"FMLF\"\u003e\u003c/a\u003e **[FMLF]** Crowd Density Estimation Using Fusion of Multi-Layer Features (**TITS**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9063540)]\n- \u003ca name=\"MLSTN\"\u003e\u003c/a\u003e **[MLSTN]** Multi-level feature fusion based Locality-Constrained Spatial Transformer network for video crowd counting (**Neurocomputing**) [[paper](https://www.sciencedirect.com/science/article/pii/S0925231220301454)](extension of [LSTN](#LSTN))\n- \u003ca name=\"SRN+PS\"\u003e\u003c/a\u003e **[SRN+PS]** Scale-Recursive Network with point supervision for crowd scene analysis (**Neurocomputing**) [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231219317795)]\n- \u003ca name=\"ASDF\"\u003e\u003c/a\u003e **[ASDF]** Counting crowds with varying densities via adaptive scenario discovery framework (**Neurocomputing**) [[paper](https://www.sciencedirect.com/science/article/pii/S0925231220302356)](extension of [ASD](#ASD))\n- \u003ca name=\"CAT-CNN\"\u003e\u003c/a\u003e **[CAT-CNN]** Crowd counting with crowd attention convolutional neural network (**Neurocomputing**) [[paper](https://www.sciencedirect.com/science/article/pii/S0925231219316662)]\n- \u003ca name=\"RRP\"\u003e\u003c/a\u003e **[RRP]** Relevant Region Prediction for Crowd Counting (**Neurocomputing**) [[paper](https://arxiv.org/abs/2005.09816)]\n- \u003ca name=\"SCAN\"\u003e\u003c/a\u003e **[SCAN]** Crowd Counting via Scale-Communicative Aggregation Networks (**Neurocomputing**) [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231220308778)](extension of [MVSAN](#MVSAN))\n- \u003ca name=\"MobileCount\"\u003e\u003c/a\u003e **[MobileCount]** MobileCount: An Efficient Encoder-Decoder Framework for Real-Time Crowd Counting (**Neurocomputing**) [[conference paper](https://link.springer.com/chapter/10.1007/978-3-030-31723-2_50)] [[journal paper](https://www.sciencedirect.com/science/article/pii/S0925231220308912)] [[code](https://github.com/SelinaFelton/MobileCount)]![GitHub stars](http://img.shields.io/github/stars/SelinaFelton/MobileCount.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"TAN\"\u003e\u003c/a\u003e **[TAN]** Fast Video Crowd Counting with a Temporal Aware Network (**Neurocomputing**) [[paper](https://arxiv.org/abs/1907.02198)]\n- \u003ca name=\"CFANet\"\u003e\u003c/a\u003e **[MH-METRONET]** MH-MetroNet—A Multi-Head CNN for Passenger-Crowd Attendance Estimation (**JImaging**) [[paper](https://www.mdpi.com/759202)][[code]( https://bitbucket.org/isasi-lecce/mh-metronet/src/master/)]\n\n### 2019\n### Conference\n- \u003ca name=\"CG-DRCN\"\u003e\u003c/a\u003e **[CG-DRCN]** Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method (**ICCV**)[[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Sindagi_Pushing_the_Frontiers_of_Unconstrained_Crowd_Counting_New_Dataset_and_ICCV_2019_paper.pdf)]\n- \u003ca name=\"ADMG\"\u003e\u003c/a\u003e **[ADMG]** Adaptive Density Map Generation for Crowd Counting (**ICCV**)[[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wan_Adaptive_Density_Map_Generation_for_Crowd_Counting_ICCV_2019_paper.pdf)]\n- \u003ca name=\"DSSINet\"\u003e\u003c/a\u003e **[DSSINet]** Crowd Counting with Deep Structured Scale Integration Network (**ICCV**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Crowd_Counting_With_Deep_Structured_Scale_Integration_Network_ICCV_2019_paper.pdf)][[code](https://github.com/Legion56/Counting-ICCV-DSSINet)] ![GitHub stars](http://img.shields.io/github/stars/Legion56/Counting-ICCV-DSSINet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"RANet\"\u003e\u003c/a\u003e **[RANet]** Relational Attention Network for Crowd Counting (**ICCV**)[[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Relational_Attention_Network_for_Crowd_Counting_ICCV_2019_paper.pdf)]\n- \u003ca name=\"ANF\"\u003e\u003c/a\u003e **[ANF]** Attentional Neural Fields for Crowd Counting (**ICCV**)[[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Attentional_Neural_Fields_for_Crowd_Counting_ICCV_2019_paper.pdf)]\n- \u003ca name=\"SPANet\"\u003e\u003c/a\u003e **[SPANet]** Learning Spatial Awareness to Improve Crowd Counting (**ICCV(oral)**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Cheng_Learning_Spatial_Awareness_to_Improve_Crowd_Counting_ICCV_2019_paper.pdf)]\n- \u003ca name=\"MBTTBF\"\u003e\u003c/a\u003e **[MBTTBF]** Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting (**ICCV**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Sindagi_Multi-Level_Bottom-Top_and_Top-Bottom_Feature_Fusion_for_Crowd_Counting_ICCV_2019_paper.pdf)]\n- \u003ca name=\"CFF\"\u003e\u003c/a\u003e **[CFF]** Counting with Focus for Free (**ICCV**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Shi_Counting_With_Focus_for_Free_ICCV_2019_paper.pdf)][[code](https://github.com/shizenglin/Counting-with-Focus-for-Free)] ![GitHub stars](http://img.shields.io/github/stars/shizenglin/Counting-with-Focus-for-Free.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"L2SM\"\u003e\u003c/a\u003e **[L2SM]** Learn to Scale: Generating Multipolar Normalized Density Map for Crowd Counting (**ICCV**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Learn_to_Scale_Generating_Multipolar_Normalized_Density_Maps_for_Crowd_ICCV_2019_paper.pdf)]\n- \u003ca name=\"S-DCNet\"\u003e\u003c/a\u003e **[S-DCNet]** From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer (**ICCV**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xiong_From_Open_Set_to_Closed_Set_Counting_Objects_by_Spatial_ICCV_2019_paper.pdf)][[code](https://github.com/xhp-hust-2018-2011/S-DCNet)]![GitHub stars](http://img.shields.io/github/stars/xhp-hust-2018-2011/S-DCNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"BL\"\u003e\u003c/a\u003e **[BL]** Bayesian Loss for Crowd Count Estimation with Point Supervision (**ICCV(oral)**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Ma_Bayesian_Loss_for_Crowd_Count_Estimation_With_Point_Supervision_ICCV_2019_paper.pdf)][[code](https://github.com/ZhihengCV/Bayesian-Crowd-Counting)] ![GitHub stars](http://img.shields.io/github/stars/ZhihengCV/Bayesian-Crowd-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"PGCNet\"\u003e\u003c/a\u003e **[PGCNet]** Perspective-Guided Convolution Networks for Crowd Counting (**ICCV**) [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Yan_Perspective-Guided_Convolution_Networks_for_Crowd_Counting_ICCV_2019_paper.pdf)][[code](https://github.com/Zhaoyi-Yan/PGCNet)]![GitHub stars](http://img.shields.io/github/stars/Zhaoyi-Yan/PGCNet.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"SACANet\"\u003e\u003c/a\u003e **[SACANet]** Crowd Counting on Images with Scale Variation and Isolated Clusters (**ICCVW**) [[paper](https://arxiv.org/abs/1909.03839)]\n- \u003ca name=\"McML\"\u003e\u003c/a\u003e **[McML]** Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting (**ACM MM**) [[paper](https://dl.acm.org/citation.cfm?doid=3343031.3350898)]\n- \u003ca name=\"DADNet\"\u003e\u003c/a\u003e **[DADNet]** DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting (**ACM MM**) [[paper](https://dl.acm.org/citation.cfm?doid=3343031.3350881)] \n- \u003ca name=\"MRNet\"\u003e\u003c/a\u003e **[MRNet]** Crowd Counting via Multi-layer Regression (**ACM MM**) [[paper](https://dl.acm.org/citation.cfm?doid=3343031.3350914)]\n- \u003ca name=\"MRCNet\"\u003e\u003c/a\u003e **[MRCNet]** MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground Imagery (**BMVCW**)[[paper](https://arxiv.org/abs/1909.12743)]\n- \u003ca name=\"E3D\"\u003e\u003c/a\u003e **[E3D]** Enhanced 3D convolutional networks for crowd counting (**BMVC**) [[paper](https://arxiv.org/abs/1908.04121)]\n- \u003ca name=\"OSSS\"\u003e\u003c/a\u003e **[OSSS]** One-Shot Scene-Specific Crowd Counting (**BMVC**) [[paper](https://bmvc2019.org/wp-content/uploads/papers/0209-paper.pdf)]\n- \u003ca name=\"RAZ-Net\"\u003e\u003c/a\u003e **[RAZ-Net]** Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization (**CVPR**) [[paper](http://www.muyadong.com/paper/cvpr19_0484.pdf)]\n- \u003ca name=\"RDNet\"\u003e\u003c/a\u003e **[RDNet]** Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization (**CVPR**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Lian_Density_Map_Regression_Guided_Detection_Network_for_RGB-D_Crowd_Counting_CVPR_2019_paper.pdf)][[code](https://github.com/svip-lab/RGBD-Counting)] ![GitHub stars](http://img.shields.io/github/stars/svip-lab/RGBD-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"RRSP\"\u003e\u003c/a\u003e **[RRSP]** Residual Regression with Semantic Prior for Crowd Counting (**CVPR**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wan_Residual_Regression_With_Semantic_Prior_for_Crowd_Counting_CVPR_2019_paper.pdf)][[code](https://github.com/jia-wan/ResidualRegression-pytorch)] ![GitHub stars](http://img.shields.io/github/stars/jia-wan/ResidualRegression-pytorch.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"MVMS\"\u003e\u003c/a\u003e **[MVMS]** Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs (**CVPR**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Wide-Area_Crowd_Counting_via_Ground-Plane_Density_Maps_and_Multi-View_Fusion_CVPR_2019_paper.pdf)] [[Project](http://visal.cs.cityu.edu.hk/research/cvpr2019wacc/)] [[Dataset\u0026Code](http://visal.cs.cityu.edu.hk/research/citystreet/)]\n- \u003ca name=\"AT-CFCN\"\u003e\u003c/a\u003e **[AT-CFCN]** Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting (**CVPR**) [[paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Leveraging_Heterogeneous_Auxiliary_Tasks_to_Assist_Crowd_Counting_CVPR_2019_paper.pdf)]\n- \u003ca name=\"TEDnet\"\u003e\u003c/a\u003e **[TEDnet]** Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks (**CVPR**) [[paper](https://arxiv.org/abs/1903.00853)]\n- \u003ca name=\"CAN\"\u003e\u003c/a\u003e **[CAN]** Context-Aware Crowd Counting (**CVPR**) [[paper](https://arxiv.org/pdf/1811.10452.pdf)] [[code](https://github.com/weizheliu/Context-Aware-Crowd-Counting)]![GitHub stars](http://img.shields.io/github/stars/weizheliu/Context-Aware-Crowd-Counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"PACNN\"\u003e\u003c/a\u003e **[PACNN]** Revisiting Perspective Information for Efficient Crowd Counting (**CVPR**)[[paper](https://arxiv.org/abs/1807.01989v3)]\n- \u003ca name=\"PSDDN\"\u003e\u003c/a\u003e **[PSDDN]** Point in, Box out: Beyond Counting Persons in Crowds (**CVPR(oral)**)[[paper](https://arxiv.org/abs/1904.01333)]\n- \u003ca name=\"ADCrowdNet\"\u003e\u003c/a\u003e **[ADCrowdNet]** ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding (**CVPR**) [[paper](https://arxiv.org/abs/1811.11968)]\n- \u003ca name=\"CCWld\"\u003e\u003c/a\u003e **[CCWld, SFCN]** Learning from Synthetic Data for Crowd Counting in the Wild (**CVPR**) [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Learning_From_Synthetic_Data_for_Crowd_Counting_in_the_Wild_CVPR_2019_paper.pdf)] [[Project](https://gjy3035.github.io/GCC-CL/)] [[arxiv](https://arxiv.org/abs/1903.03303)] ![GitHub stars](http://img.shields.io/github/stars/gjy3035/GCC-CL.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DG-GAN\"\u003e\u003c/a\u003e **[DG-GAN]** Dense Crowd Counting Convolutional Neural Networks with Minimal Data using Semi-Supervised Dual-Goal Generative Adversarial Networks (**CVPRW**)[[paper](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Weakly%20Supervised%20Learning%20for%20Real-World%20Computer%20Vision%20Applications/Olmschenk_Dense_Crowd_Counting_Convolutional_Neural_Networks_with_Minimal_Data_using_CVPRW_2019_paper.pdf)]\n- \u003ca name=\"GSP\"\u003e\u003c/a\u003e **[GSP]** Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images (**CVPRW**)[[paper](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Deep%20Vision%20Workshop/Aich_Global_Sum_Pooling_A_Generalization_Trick_for_Object_Counting_with_CVPRW_2019_paper.pdf)]\n- \u003ca name=\"IA-DNN\"\u003e\u003c/a\u003e **[IA-DNN]** Inverse Attention Guided Deep Crowd Counting Network (**AVSS Best Paper**) [[paper](https://arxiv.org/abs/1907.01193)]\n- \u003ca name=\"MTCNet\"\u003e\u003c/a\u003e **[MTCNet]** MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation (**AVSS**) [[paper](https://arxiv.org/abs/1908.08652)]\n- \u003ca name=\"CODA\"\u003e\u003c/a\u003e **[CODA]** CODA: Counting Objects via Scale-aware Adversarial Density Adaption (**ICME**) [[paper](https://arxiv.org/abs/1903.10442)][[code](https://github.com/Willy0919/CODA)]![GitHub stars](http://img.shields.io/github/stars/Willy0919/CODA.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"LSTN\"\u003e\u003c/a\u003e **[LSTN]** Locality-Constrained Spatial Transformer Network for Video Crowd Counting (**ICME(oral)**)  [[paper](https://arxiv.org/abs/1907.07911)]\n- \u003ca name=\"DRD\"\u003e\u003c/a\u003e **[DRD]** Dynamic Region Division for Adaptive Learning Pedestrian Counting (**ICME**) [[paper](https://arxiv.org/abs/1908.03978)]\n- \u003ca name=\"MVSAN\"\u003e\u003c/a\u003e **[MVSAN]** Crowd Counting via Multi-View Scale Aggregation Networks (**ICME**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8784912)]\n- \u003ca name=\"ASD\"\u003e\u003c/a\u003e **[ASD]** Adaptive Scenario Discovery for Crowd Counting (**ICASSP**) [[paper](https://arxiv.org/abs/1812.02393)]\n- \u003ca name=\"SAAN\"\u003e\u003c/a\u003e **[SAAN]** Crowd Counting Using Scale-Aware Attention Networks (**WACV**) [[paper](http://www.cs.umanitoba.ca/~ywang/papers/wacv19.pdf)]\n- \u003ca name=\"SPN\"\u003e\u003c/a\u003e **[SPN]** Scale Pyramid Network for Crowd Counting (**WACV**) [[paper](http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8642793)]\n- \u003ca name=\"GWTA-CCNN\"\u003e\u003c/a\u003e **[GWTA-CCNN]** Almost Unsupervised Learning for Dense Crowd Counting (**AAAI**) [[paper](http://val.serc.iisc.ernet.in/valweb/papers/AAAI_2019_WTACNN.pdf)]\n- \u003ca name=\"GPC\"\u003e\u003c/a\u003e **[GPC]** Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation (**IROS**) [[paper](https://arxiv.org/abs/1803.08805)]\n- \u003ca name=\"AM-CNN\"\u003e\u003c/a\u003e **[AM-CNN]** Attention to Head Locations for Crowd Counting (**ICIG**) [[paper](https://arxiv.org/abs/1806.10287)]\n- \u003ca name=\"CRDNet\"\u003e\u003c/a\u003e **[CRDNet]** Cascaded Residual Density Network for Crowd Counting (**ICIP**) [[paper](https://arxiv.org/abs/2107.13718)]\n\n### Journal\n- \u003ca name=\"D-ConvNet\"\u003e\u003c/a\u003e **[D-ConvNet]** Nonlinear Regression via Deep Negative Correlation Learning (**T-PAMI**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8850209)](extension of [D-ConvNet](#D-ConvNet))[[Project](https://mmcheng.net/dncl/)]\n- \u003ca name=\"SL2R\"\u003e\u003c/a\u003e  **[SL2R]** Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank (**T-PAMI**) [[paper](https://arxiv.org/abs/1902.06285)](extension of [L2R](#L2R))\n- \u003ca name=\"PCC-Net\"\u003e\u003c/a\u003e **[PCC-Net]** PCC Net: Perspective Crowd Counting via Spatial Convolutional Network (**T-CSVT**) [[paper](https://arxiv.org/abs/1905.10085)] [[code](https://github.com/gjy3035/PCC-Net)]![GitHub stars](http://img.shields.io/github/stars/gjy3035/PCC-Net.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"Deem\"\u003e\u003c/a\u003e **[Deem]** Scale-Aware Crowd Counting via Depth-Embedded Convolutional Neural Networks (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8846233)]\n- \u003ca name=\"CLPC\"\u003e\u003c/a\u003e **[CLPC]** Cross-Line Pedestrian Counting Based on Spatially-Consistent Two-Stage Local Crowd Density Estimation and Accumulation (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8295124)]\n- \u003ca name=\"MAN\"\u003e\u003c/a\u003e **[MAN]** Mask-aware networks for crowd counting (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8796427)]\n- \u003ca name=\"\"\u003e\u003c/a\u003eGeneralizing semi-supervised generative adversarial networks to regression using feature contrasting (**CVIU**)[[paper](https://arxiv.org/abs/1811.11269)]\n- \u003ca name=\"CCLL\"\u003e\u003c/a\u003e **[CCLL]** Crowd Counting With Limited Labeling Through Submodular Frame Selection (**T-ITS**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8360780)]\n- \u003ca name=\"MLCNN\"\u003e\u003c/a\u003e **[GMLCNN]** Learning Multi-Level Density Maps for Crowd Counting (**T-NNLS**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8848475)]\n- \u003ca name=\"HA-CCN\"\u003e\u003c/a\u003e **[HA-CCN]** HA-CCN: Hierarchical Attention-based Crowd Counting Network (**TIP**) [[paper](https://arxiv.org/abs/1907.10255)]\n- \u003ca name=\"PaDNet\"\u003e\u003c/a\u003e **[PaDNet]** PaDNet: Pan-Density Crowd Counting (**TIP**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8897143)]\n- \u003ca name=\"LDL\"\u003e\u003c/a\u003e **[LDL]** Indoor Crowd Counting by Mixture of Gaussians Label Distribution Learning (**TIP**) [[paper](http://palm.seu.edu.cn/xgeng/files/tip19.pdf)]\n- \u003ca name=\"ACSPNet\"\u003e\u003c/a\u003e **[ACSPNet]** Atrous convolutions spatial pyramid network for crowd counting and density estimation (**Neurocomputing**) [[paper](https://www.sciencedirect.com/science/article/pii/S0925231219304059)]\n- \u003ca name=\"DDCN\"\u003e\u003c/a\u003e **[DDCN]** Removing background interference for crowd counting via de-background detail convolutional network (**Neurocomputing**) [[paper](https://www.sciencedirect.com/science/article/pii/S0925231218315042)]\n- \u003ca name=\"MRA-CNN\"\u003e\u003c/a\u003e **[MRA-CNN]** Multi-resolution attention convolutional neural network for crowd counting (**Neurocomputing**) [[paper](https://www.sciencedirect.com/science/article/pii/S0925231218312542)]\n- \u003ca name=\"ACM-CNN\"\u003e\u003c/a\u003e **[ACM-CNN]** Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs (**Neurocomputing**) [[paper](https://arxiv.org/abs/1908.02797)]\n- \u003ca name=\"SDA-MCNN\"\u003e\u003c/a\u003e **[SDA-MCNN]** Counting crowds using a scale-distribution-aware network and adaptive human-shaped kernel (**Neurocomputing**) [[paper](https://www.sciencedirect.com/science/article/pii/S0925231219314651)]\n- \u003ca name=\"SCAR\"\u003e\u003c/a\u003e **[SCAR]** SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting (**Neurocomputing**) [[paper](https://arxiv.org/abs/1908.03716)][[code](https://github.com/gjy3035/SCAR)]![GitHub stars](http://img.shields.io/github/stars/gjy3035/SCAR.svg?logo=github\u0026label=Stars)\n\n### 2018\n### Conference\n- \u003ca name=\"SANet\"\u003e\u003c/a\u003e **[SANet]** Scale Aggregation Network for Accurate and Efficient Crowd Counting (**ECCV**) [[paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Xinkun_Cao_Scale_Aggregation_Network_ECCV_2018_paper.pdf)]\n- \u003ca name=\"ic-CNN\"\u003e\u003c/a\u003e **[ic-CNN]** Iterative Crowd Counting (**ECCV**) [[paper](https://arxiv.org/abs/1807.09959)]\n- \u003ca name=\"CL\"\u003e\u003c/a\u003e **[CL]** Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (**ECCV**) [[paper](https://arxiv.org/abs/1808.01050)]\n- \u003ca name=\"LCFCN\"\u003e\u003c/a\u003e **[LCFCN]**  Where are the Blobs: Counting by Localization with Point Supervision (**ECCV**) [[paper](https://arxiv.org/abs/1807.09856)] [[code](https://github.com/ElementAI/LCFCN)]![GitHub stars](http://img.shields.io/github/stars/ElementAI/LCFCN.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CSR\"\u003e\u003c/a\u003e **[CSR]**  CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes (**CVPR**) [[paper](https://arxiv.org/abs/1802.10062)] [[code](https://github.com/leeyeehoo/CSRNet-pytorch)]![GitHub stars](http://img.shields.io/github/stars/leeyeehoo/CSRNet-pytorch.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"L2R\"\u003e\u003c/a\u003e  **[L2R]** Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (**CVPR**) [[paper](https://arxiv.org/abs/1803.03095)] [[code](https://github.com/xialeiliu/CrowdCountingCVPR18)] ![GitHub stars](http://img.shields.io/github/stars/xialeiliu/CrowdCountingCVPR18.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"ACSCP\"\u003e\u003c/a\u003e **[ACSCP]**  Crowd Counting via Adversarial Cross-Scale Consistency Pursuit  (**CVPR**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_Crowd_Counting_via_CVPR_2018_paper.pdf)]   [unofficial code: [PyTorch](https://github.com/RQuispeC/pytorch-ACSCP)]![GitHub stars](http://img.shields.io/github/stars/RQuispeC/pytorch-ACSCP.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"DecideNet\"\u003e\u003c/a\u003e **[DecideNet]** DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density (**CVPR**) [[paper](https://arxiv.org/abs/1712.06679)]\n- \u003ca name=\"AMDCN\"\u003e\u003c/a\u003e  **[AMDCN]** An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting (**CVPRW**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w6/Deb_An_Aggregated_Multicolumn_CVPR_2018_paper.pdf)] [[code](https://github.com/diptodip/counting)] ![GitHub stars](http://img.shields.io/github/stars/diptodip/counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"D-ConvNet\"\u003e\u003c/a\u003e **[D-ConvNet]** Crowd Counting with Deep Negative Correlation Learning (**CVPR**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Shi_Crowd_Counting_With_CVPR_2018_paper.pdf)] [[code](https://github.com/shizenglin/Deep-NCL)]![GitHub stars](http://img.shields.io/github/stars/shizenglin/Deep-NCL.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"IG-CNN\"\u003e\u003c/a\u003e **[IG-CNN]** Divide and Grow: Capturing Huge Diversity in Crowd Images with\nIncrementally Growing CNN (**CVPR**) [[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Sam_Divide_and_Grow_CVPR_2018_paper.pdf)]\n- \u003ca name=\"SCNet\"\u003e\u003c/a\u003e**[SCNet]** In Defense of Single-column Networks for Crowd Counting (**BMVC**) [[paper](https://arxiv.org/abs/1808.06133)]\n- \u003ca name=\"AFP\"\u003e\u003c/a\u003e**[AFP]** Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid (**BMVC**) [[paper](https://arxiv.org/abs/1805.06115)]\n- \u003ca name=\"DRSAN\"\u003e\u003c/a\u003e**[DRSAN]** Crowd Counting using Deep Recurrent Spatial-Aware Network (**IJCAI**) [[paper](https://arxiv.org/abs/1807.00601)]\n- \u003ca name=\"TDF-CNN\"\u003e\u003c/a\u003e**[TDF-CNN]** Top-Down Feedback for Crowd Counting Convolutional Neural Network (**AAAI**) [[paper](https://arxiv.org/abs/1807.08881)]\n- \u003ca name=\"CAC\"\u003e\u003c/a\u003e**[CAC]** Class-Agnostic Counting (**ACCV**) [[paper](https://arxiv.org/abs/1811.00472)] [[code](https://github.com/erikalu/class-agnostic-counting)]![GitHub stars](http://img.shields.io/github/stars/erikalu/class-agnostic-counting.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"A-CCNN\"\u003e\u003c/a\u003e **[A-CCNN]** A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting (**ICIP**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8451399)]\n- \u003ca name=\"\"\u003e\u003c/a\u003e Crowd Counting with Fully Convolutional Neural Network (**ICIP**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8451787)]\n- \u003ca name=\"MS-GAN\"\u003e\u003c/a\u003e **[MS-GAN]** Multi-scale Generative Adversarial Networks for Crowd Counting (**ICPR**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8545683)]\n- \u003ca name=\"DR-ResNet\"\u003e\u003c/a\u003e **[DR-ResNet]** A Deeply-Recursive Convolutional Network for Crowd Counting (**ICASSP**) [[paper](https://arxiv.org/abs/1805.05633)] \n- \u003ca name=\"GAN-MTR\"\u003e\u003c/a\u003e **[GAN-MTR]** Crowd Counting With Minimal Data Using Generative Adversarial Networks For Multiple Target Regression (**WACV**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8354235)]\n- \u003ca name=\"SaCNN\"\u003e\u003c/a\u003e **[SaCNN]** Crowd counting via scale-adaptive convolutional neural network (**WACV**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8354231)] [[code](https://github.com/miao0913/SaCNN-CrowdCounting-Tencent_Youtu)]![GitHub stars](http://img.shields.io/github/stars/miao0913/SaCNN-CrowdCounting-Tencent_Youtu.svg?logo=github\u0026label=Stars)\n\n### Journal\n- \u003ca name=\"BSAD\"\u003e\u003c/a\u003e **[BSAD]** Body Structure Aware Deep Crowd Counting (**TIP**) [[paper](http://mac.xmu.edu.cn/rrji/papers/IP%202018-Body.pdf)] \n- \u003ca name=\"NetVLAD\"\u003e\u003c/a\u003e **[NetVLAD]** Multiscale Multitask Deep NetVLAD for Crowd Counting (**TII**) [[paper](https://staff.fnwi.uva.nl/z.shi/files/counting-netvlad.pdf)] [[code](https://github.com/shizenglin/Multitask-Multiscale-Deep-NetVLAD)]![GitHub stars](http://img.shields.io/github/stars/shizenglin/Multitask-Multiscale-Deep-NetVLAD.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"W-VLAD\"\u003e\u003c/a\u003e **[W-VLAD]** Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps (**T-CSVT**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=7778134)]\n- \u003ca name=\"Improved SaCNN\"\u003e\u003c/a\u003e **[Improved SaCNN]** Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network (**IEEE Access**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8643345)]\n- \u003ca name=\"DA-Net\"\u003e\u003c/a\u003e **[DA-Net]** DA-Net: Learning the Fine-Grained Density Distribution With Deformation Aggregation Network (**IEEE Access**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8497050)][[code](https://github.com/BigTeacher-777/DA-Net)]![GitHub stars](http://img.shields.io/github/stars/BigTeacher-777/DA-Net.svg?logo=github\u0026label=Stars)\n\n### 2017\n### Conference\n- \u003ca name=\"SCNN\"\u003e\u003c/a\u003e **[Switching CNN]** Switching Convolutional Neural Network for Crowd Counting (**CVPR**) [[paper](https://arxiv.org/abs/1708.00199)] [[code](https://github.com/val-iisc/crowd-counting-scnn)]![GitHub stars](http://img.shields.io/github/stars/val-iisc/crowd-counting-scnn.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"CP-CNN\"\u003e\u003c/a\u003e **[CP-CNN]** Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs (**ICCV**) [[paper](https://arxiv.org/abs/1708.00953)]\n- \u003ca name=\"ConvLSTM\"\u003e\u003c/a\u003e **[ConvLSTM]** Spatiotemporal Modeling for Crowd Counting in Videos (**ICCV**) [[paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Xiong_Spatiotemporal_Modeling_for_ICCV_2017_paper.pdf)]\n- \u003ca name=\"CMTL\"\u003e\u003c/a\u003e **[CMTL]** CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting (**AVSS**) [[paper](https://arxiv.org/abs/1707.09605)] [[code](https://github.com/svishwa/crowdcount-cascaded-mtl)]![GitHub stars](http://img.shields.io/github/stars/svishwa/crowdcount-cascaded-mtl.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"ResnetCrowd\"\u003e\u003c/a\u003e **[ResnetCrowd]** ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification (**AVSS**) [[paper](https://arxiv.org/abs/1705.10698)]\n- \u003ca name=\"ACNN\"\u003e\u003c/a\u003e **[ACNN]** Incorporating Side Information by Adaptive Convolution (**NeurIPS**) [[paper](http://papers.nips.cc/paper/6976-incorporating-side-information-by-adaptive-convolution.pdf)][[Project](http://visal.cs.cityu.edu.hk/research/acnn/)]\n- \u003ca name=\"MSCNN\"\u003e\u003c/a\u003e **[MSCNN]** Multi-scale Convolution Neural Networks for Crowd Counting (**ICIP**) [[paper](https://arxiv.org/abs/1702.02359)] [[code](https://github.com/Ling-Bao/mscnn)]![GitHub stars](http://img.shields.io/github/stars/Ling-Bao/mscnn.svg?logo=github\u0026label=Stars)\n- \u003ca name=\"FCNCC\"\u003e\u003c/a\u003e **[FCNCC]** Fully Convolutional Crowd Counting On Highly Congested Scenes (**VISAPP**) [[paper](https://arxiv.org/abs/1612.00220)]\n\n### Journal\n- \u003ca name=\"DAL-SVR\"\u003e\u003c/a\u003e **[DAL-SVR]** Boosting deep attribute learning via support vector regression for fast moving crowd counting (**PR Letters**) [[paper](https://www.sciencedirect.com/science/article/pii/S0167865517304415)]\n- \u003ca name=\"CNN-MRF\"\u003e\u003c/a\u003e **[CNN-MRF]** Image Crowd Counting Using Convolutional Neural Network and Markov Random Field (**JACII**) [[paper](https://arxiv.org/abs/1706.03686)] [[code](https://github.com/hankong/crowd-counting)]![GitH","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgjy3035%2FAwesome-Crowd-Counting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgjy3035%2FAwesome-Crowd-Counting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgjy3035%2FAwesome-Crowd-Counting/lists"}