Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/amusi/awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
https://github.com/amusi/awesome-object-detection

List: awesome-object-detection

computer-vision deep-learning detection object-detection object-localisation

Last synced: 5 days ago
JSON representation

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

Lists

README 

        
# object-detection



[TOC]



This is a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to [Date](Date.md).



- R-CNN

- Fast R-CNN

- Faster R-CNN

- Mask R-CNN

- Light-Head R-CNN

- Cascade R-CNN

- SPP-Net

- YOLO

- YOLOv2

- YOLOv3

- YOLT

- SSD

- DSSD

- FSSD

- ESSD

- MDSSD

- Pelee

- Fire SSD

- R-FCN

- FPN

- DSOD

- RetinaNet

- MegDet

- RefineNet

- DetNet

- SSOD

- CornerNet

- M2Det

- 3D Object Detection

- ZSD（Zero-Shot Object Detection）

- OSD（One-Shot object Detection）

- Weakly Supervised Object Detection

- Softer-NMS

- 2018

- 2019

- Other



Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html



# Survey



**Imbalance Problems in Object Detection: A Review**



- intro: under review at TPAMI

- arXiv: 



**Recent Advances in Deep Learning for Object Detection**



- intro: From 2013 (OverFeat) to 2019 (DetNAS)

- arXiv: 



**A Survey of Deep Learning-based Object Detection**



- intro：From Fast R-CNN to NAS-FPN



- arXiv：



**Object Detection in 20 Years: A Survey**



- intro：This work has been submitted to the IEEE TPAMI for possible publication

- arXiv：



**《Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks》**



- intro: awesome



- arXiv: https://arxiv.org/abs/1809.03193



**《Deep Learning for Generic Object Detection: A Survey》**



- intro: Submitted to IJCV 2018

- arXiv: https://arxiv.org/abs/1809.02165



# Papers&Codes



## R-CNN



**Rich feature hierarchies for accurate object detection and semantic segmentation**



- intro: R-CNN

- arxiv: 

- supp: 

- slides: 

- slides: 

- github: 

- notes: 

- caffe-pr("Make R-CNN the Caffe detection example"): 



## Fast R-CNN



**Fast R-CNN**



- arxiv: 

- slides: 

- github: 

- github(COCO-branch): 

- webcam demo: 

- notes: 

- notes: 

- github("Fast R-CNN in MXNet"): 

- github: 

- github: 

- github: 



**A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection**



- intro: CVPR 2017

- arxiv: 

- paper: 

- github(Caffe): 



## Faster R-CNN



**Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks**



- intro: NIPS 2015

- arxiv: 

- gitxiv: 

- slides: 

- github(official, Matlab): 

- github(Caffe): 

- github(MXNet): 

- github(PyTorch--recommend): 

- github: 

- github(Torch):: 

- github(Torch):: 

- github(TensorFlow): 

- github(TensorFlow): 

- github(C++ demo): 

- github(Keras): 

- github: 

- github(C++): 



**R-CNN minus R**



- intro: BMVC 2015

- arxiv: 



**Faster R-CNN in MXNet with distributed implementation and data parallelization**



- github: 



**Contextual Priming and Feedback for Faster R-CNN**



- intro: ECCV 2016. Carnegie Mellon University

- paper: 

- poster: 



**An Implementation of Faster RCNN with Study for Region Sampling**



- intro: Technical Report, 3 pages. CMU

- arxiv: 

- github: 

- github: https://github.com/ruotianluo/pytorch-faster-rcnn



**Interpretable R-CNN**



- intro: North Carolina State University & Alibaba

- keywords: AND-OR Graph (AOG)

- arxiv: 



**Domain Adaptive Faster R-CNN for Object Detection in the Wild**



- intro: CVPR 2018. ETH Zurich & ESAT/PSI

- arxiv: 



## Mask R-CNN



- arxiv: 

- github(Keras): https://github.com/matterport/Mask_RCNN

- github(Caffe2): https://github.com/facebookresearch/Detectron

- github(Pytorch): 

- github(MXNet): https://github.com/TuSimple/mx-maskrcnn

- github(Chainer): https://github.com/DeNA/Chainer_Mask_R-CNN



## Light-Head R-CNN



**Light-Head R-CNN: In Defense of Two-Stage Object Detector**



- intro: Tsinghua University & Megvii Inc

- arxiv: 

- github(offical): https://github.com/zengarden/light_head_rcnn

- github: 



## Cascade R-CNN



**Cascade R-CNN: Delving into High Quality Object Detection**



- arxiv: 

- github: 



## SPP-Net



**Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition**



- intro: ECCV 2014 / TPAMI 2015

- arxiv: 

- github: 

- notes: 



**DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection**



- intro: PAMI 2016

- intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations

- project page: 

- arxiv: 



**Object Detectors Emerge in Deep Scene CNNs**



- intro: ICLR 2015

- arxiv: 

- paper: 

- paper: 

- slides: 



**segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection**



- intro: CVPR 2015

- project(code+data): 

- arxiv: 

- github: 



**Object Detection Networks on Convolutional Feature Maps**



- intro: TPAMI 2015

- keywords: NoC

- arxiv: 



**Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction**



- arxiv: 

- slides: 

- github: 



**DeepBox: Learning Objectness with Convolutional Networks**



- keywords: DeepBox

- arxiv: 

- github: 



## YOLO



**You Only Look Once: Unified, Real-Time Object Detection**



[![img](https://camo.githubusercontent.com/e69d4118b20a42de4e23b9549f9a6ec6dbbb0814/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67)](https://camo.githubusercontent.com/e69d4118b20a42de4e23b9549f9a6ec6dbbb0814/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67)



- arxiv: 

- code: 

- github: 

- blog: 

- slides: 

- reddit: 

- github: 

- github: 

- github: 

- github: 

- github: 

- github: 

- github: 

- github: 



**darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++**



- blog: 

- github: 



**Start Training YOLO with Our Own Data**



[![img](https://camo.githubusercontent.com/2f99b692dd7ce47d7832385f3e8a6654e680d92a/687474703a2f2f6775616e6768616e2e696e666f2f626c6f672f656e2f77702d636f6e74656e742f75706c6f6164732f323031352f31322f696d616765732d34302e6a7067)](https://camo.githubusercontent.com/2f99b692dd7ce47d7832385f3e8a6654e680d92a/687474703a2f2f6775616e6768616e2e696e666f2f626c6f672f656e2f77702d636f6e74656e742f75706c6f6164732f323031352f31322f696d616765732d34302e6a7067)



- intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.

- blog: 

- github: 



**YOLO: Core ML versus MPSNNGraph**



- intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.

- blog: 

- github: 



**TensorFlow YOLO object detection on Android**



- intro: Real-time object detection on Android using the YOLO network with TensorFlow

- github: 



**Computer Vision in iOS – Object Detection**



- blog: 

- github:



## YOLOv2



**YOLO9000: Better, Faster, Stronger**



- arxiv: 

- code:     https://pjreddie.com/darknet/yolov2/

- github(Chainer): 

- github(Keras): 

- github(PyTorch): 

- github(Tensorflow): 

- github(Windows): 

- github: 

- github: 

- github(TensorFlow): 

- github(Keras): 

- github(Keras): 

- github(TensorFlow): 



**darknet_scripts**



- intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?

- github: 



**Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2**



- github: 



**LightNet: Bringing pjreddie's DarkNet out of the shadows**



**YOLO v2 Bounding Box Tool**



- intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.

- github: 



**Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors**



- intro: **LRM** is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.

- arxiv: https://arxiv.org/abs/1804.04606



**Object detection at 200 Frames Per Second**



- intro: faster than Tiny-Yolo-v2

- arxiv: https://arxiv.org/abs/1805.06361



**Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras**



- intro: YOLE--Object Detection in Neuromorphic Cameras

- arxiv:https://arxiv.org/abs/1805.07931



**OmniDetector: With Neural Networks to Bounding Boxes**



- intro: a person detector on n fish-eye images of indoor scenes（NIPS 2018）

- arxiv:https://arxiv.org/abs/1805.08503

- datasets:https://gitlab.com/omnidetector/omnidetector



## YOLOv3



**YOLOv3: An Incremental Improvement**



- arxiv:https://arxiv.org/abs/1804.02767

- paper:https://pjreddie.com/media/files/papers/YOLOv3.pdf

- code: 

- github(Official):https://github.com/pjreddie/darknet

- github:https://github.com/mystic123/tensorflow-yolo-v3

- github:https://github.com/experiencor/keras-yolo3

- github:https://github.com/qqwweee/keras-yolo3

- github:https://github.com/marvis/pytorch-yolo3

- github:https://github.com/ayooshkathuria/pytorch-yolo-v3

- github:https://github.com/ayooshkathuria/YOLO_v3_tutorial_from_scratch

- github:https://github.com/eriklindernoren/PyTorch-YOLOv3

- github:https://github.com/ultralytics/yolov3

- github:https://github.com/BobLiu20/YOLOv3_PyTorch

- github:https://github.com/andy-yun/pytorch-0.4-yolov3

- github:https://github.com/DeNA/PyTorch_YOLOv3



## YOLT



**You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery**



- intro: Small Object Detection



- arxiv:https://arxiv.org/abs/1805.09512

- github:https://github.com/avanetten/yolt



## SSD



**SSD: Single Shot MultiBox Detector**



[![img](https://camo.githubusercontent.com/ad9b147ed3a5f48ffb7c3540711c15aa04ce49c6/687474703a2f2f7777772e63732e756e632e6564752f7e776c69752f7061706572732f7373642e706e67)](https://camo.githubusercontent.com/ad9b147ed3a5f48ffb7c3540711c15aa04ce49c6/687474703a2f2f7777772e63732e756e632e6564752f7e776c69752f7061706572732f7373642e706e67)



- intro: ECCV 2016 Oral

- arxiv: 

- paper: 

- slides: [http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf](http://www.cs.unc.edu/~wliu/papers/ssd_eccv2016_slide.pdf)

- github(Official): 

- video: 

- github: 

- github: 

- github: 

- github: 

- github: 

- github(Caffe): 



**What's the diffience in performance between this new code you pushed and the previous code? #327**



## DSSD



**DSSD : Deconvolutional Single Shot Detector**



- intro: UNC Chapel Hill & Amazon Inc

- arxiv: 

- github: 

- github: 

- demo: 



**Enhancement of SSD by concatenating feature maps for object detection**



- intro: rainbow SSD (R-SSD)

- arxiv: 



**Context-aware Single-Shot Detector**



- keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)

- arxiv: 



**Feature-Fused SSD: Fast Detection for Small Objects**



## FSSD



**FSSD: Feature Fusion Single Shot Multibox Detector**



**Weaving Multi-scale Context for Single Shot Detector**



- intro: WeaveNet

- keywords: fuse multi-scale information

- arxiv: 



## ESSD



**Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network**



**Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection**



## MDSSD



**MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects**



- arxiv: https://arxiv.org/abs/1805.07009



## Pelee



**Pelee: A Real-Time Object Detection System on Mobile Devices**



https://github.com/Robert-JunWang/Pelee



- intro: (ICLR 2018 workshop track)



- arxiv: https://arxiv.org/abs/1804.06882

- github: https://github.com/Robert-JunWang/Pelee



## Fire SSD



**Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device**



- intro:low cost, fast speed and high mAP on  factor edge computing devices



- arxiv:https://arxiv.org/abs/1806.05363



## R-FCN



**R-FCN: Object Detection via Region-based Fully Convolutional Networks**



- arxiv: 

- github: 

- github(MXNet): 

- github: 

- github: 

- github: 

- github: 



**R-FCN-3000 at 30fps: Decoupling Detection and Classification**



**Recycle deep features for better object detection**



- arxiv: 



## FPN



**Feature Pyramid Networks for Object Detection**



- intro: Facebook AI Research

- arxiv: 



**Action-Driven Object Detection with Top-Down Visual Attentions**



- arxiv: 



**Beyond Skip Connections: Top-Down Modulation for Object Detection**



- intro: CMU & UC Berkeley & Google Research

- arxiv: 



**Wide-Residual-Inception Networks for Real-time Object Detection**



- intro: Inha University

- arxiv: 



**Attentional Network for Visual Object Detection**



- intro: University of Maryland & Mitsubishi Electric Research Laboratories

- arxiv: 



**Learning Chained Deep Features and Classifiers for Cascade in Object Detection**



- keykwords: CC-Net

- intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007

- arxiv: 



**DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling**



- intro: ICCV 2017 (poster)

- arxiv: 



**Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries**



- intro: CVPR 2017

- arxiv: 



**Spatial Memory for Context Reasoning in Object Detection**



- arxiv: 



**Accurate Single Stage Detector Using Recurrent Rolling Convolution**



- intro: CVPR 2017. SenseTime

- keywords: Recurrent Rolling Convolution (RRC)

- arxiv: 

- github: 



**Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection**



**LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems**



- intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc

- arxiv: 



**Point Linking Network for Object Detection**



- intro: Point Linking Network (PLN)

- arxiv: 



**Perceptual Generative Adversarial Networks for Small Object Detection**



**Few-shot Object Detection**



**Yes-Net: An effective Detector Based on Global Information**



**SMC Faster R-CNN: Toward a scene-specialized multi-object detector**



**Towards lightweight convolutional neural networks for object detection**



**RON: Reverse Connection with Objectness Prior Networks for Object Detection**



- intro: CVPR 2017

- arxiv: 

- github: 



**Mimicking Very Efficient Network for Object Detection**



- intro: CVPR 2017. SenseTime & Beihang University

- paper: 



**Residual Features and Unified Prediction Network for Single Stage Detection**



**Deformable Part-based Fully Convolutional Network for Object Detection**



- intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC

- arxiv: 



**Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors**



- intro: ICCV 2017

- arxiv: 



**Recurrent Scale Approximation for Object Detection in CNN**



- intro: ICCV 2017

- keywords: Recurrent Scale Approximation (RSA)

- arxiv: 

- github: 



## DSOD



**DSOD: Learning Deeply Supervised Object Detectors from Scratch**



![img](https://user-images.githubusercontent.com/3794909/28934967-718c9302-78b5-11e7-89ee-8b514e53e23c.png)



- intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China

- arxiv: 

- github: 

- github:https://github.com/Windaway/DSOD-Tensorflow

- github:https://github.com/chenyuntc/dsod.pytorch



**Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids**



- arxiv:https://arxiv.org/abs/1712.00886

- github:https://github.com/szq0214/GRP-DSOD



**Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages**



- intro: BMVC 2018

- arXiv: https://arxiv.org/abs/1807.11013



**Object Detection from Scratch with Deep Supervision**



- intro: This is an extended version of DSOD

- arXiv: https://arxiv.org/abs/1809.09294



## RetinaNet



**Focal Loss for Dense Object Detection**



- intro: ICCV 2017 Best student paper award. Facebook AI Research

- keywords: RetinaNet

- arxiv: 



**CoupleNet: Coupling Global Structure with Local Parts for Object Detection**



- intro: ICCV 2017

- arxiv: 



**Incremental Learning of Object Detectors without Catastrophic Forgetting**



- intro: ICCV 2017. Inria

- arxiv: 



**Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection**



**StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection**



**Dynamic Zoom-in Network for Fast Object Detection in Large Images**



**Zero-Annotation Object Detection with Web Knowledge Transfer**



- intro: NTU, Singapore & Amazon

- keywords: multi-instance multi-label domain adaption learning framework

- arxiv: 



## MegDet



**MegDet: A Large Mini-Batch Object Detector**



- intro: Peking University & Tsinghua University & Megvii Inc

- arxiv: 



**Receptive Field Block Net for Accurate and Fast Object Detection**



- intro: RFBNet

- arxiv: 

- github: 



**An Analysis of Scale Invariance in Object Detection - SNIP**



- arxiv: 

- github: 



**Feature Selective Networks for Object Detection**



**Learning a Rotation Invariant Detector with Rotatable Bounding Box**



- arxiv: 

- github: 



**Scalable Object Detection for Stylized Objects**



- intro: Microsoft AI & Research Munich

- arxiv: 



**Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids**



- arxiv: 

- github: 



**Deep Regionlets for Object Detection**



- keywords: region selection network, gating network

- arxiv: 



**Training and Testing Object Detectors with Virtual Images**



- intro: IEEE/CAA Journal of Automatica Sinica

- arxiv: 



**Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video**



- keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation

- arxiv: 



**Spot the Difference by Object Detection**



- intro: Tsinghua University & JD Group

- arxiv: 



**Localization-Aware Active Learning for Object Detection**



- arxiv: 



**Object Detection with Mask-based Feature Encoding**



- arxiv: 



**LSTD: A Low-Shot Transfer Detector for Object Detection**



- intro: AAAI 2018

- arxiv: 



**Pseudo Mask Augmented Object Detection**



**Revisiting RCNN: On Awakening the Classification Power of Faster RCNN**



**Learning Region Features for Object Detection**



- intro: Peking University & MSRA

- arxiv: 



**Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection**



- intro: Singapore Management University & Zhejiang University

- arxiv: 



**Object Detection for Comics using Manga109 Annotations**



- intro: University of Tokyo & National Institute of Informatics, Japan

- arxiv: 



**Task-Driven Super Resolution: Object Detection in Low-resolution Images**



- arxiv: 



**Transferring Common-Sense Knowledge for Object Detection**



- arxiv: 



**Multi-scale Location-aware Kernel Representation for Object Detection**



- intro: CVPR 2018

- arxiv: 

- github: 



**Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors**



- intro: National University of Defense Technology

- arxiv: https://arxiv.org/abs/1804.04606



**Robust Physical Adversarial Attack on Faster R-CNN Object Detector**



- arxiv: https://arxiv.org/abs/1804.05810



## RefineNet



**Single-Shot Refinement Neural Network for Object Detection**



- intro: CVPR 2018



- arxiv: 

- github: 

- github: https://github.com/lzx1413/PytorchSSD

- github: https://github.com/ddlee96/RefineDet_mxnet

- github: https://github.com/MTCloudVision/RefineDet-Mxnet



## DetNet



**DetNet: A Backbone network for Object Detection**



- intro: Tsinghua University & Face++

- arxiv: https://arxiv.org/abs/1804.06215



## SSOD



**Self-supervisory Signals for Object Discovery and Detection**



- Google Brain

- arxiv:https://arxiv.org/abs/1806.03370



## CornerNet



**CornerNet: Detecting Objects as Paired Keypoints**



- intro: ECCV 2018

- arXiv: https://arxiv.org/abs/1808.01244

- github: 



## M2Det



**M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network**



- intro: AAAI 2019

- arXiv: https://arxiv.org/abs/1811.04533

- github: https://github.com/qijiezhao/M2Det



## 3D Object Detection



**3D Backbone Network for 3D Object Detection**



- arXiv: https://arxiv.org/abs/1901.08373



**LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs**



- arxiv: https://arxiv.org/abs/1805.04902

- github: https://github.com/CPFL/Autoware/tree/feature/cnn_lidar_detection



## ZSD（Zero-Shot Object Detection）



**Zero-Shot Detection**



- intro: Australian National University

- keywords: YOLO

- arxiv: 



**Zero-Shot Object Detection**



- arxiv: https://arxiv.org/abs/1804.04340



**Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts**



- arxiv: https://arxiv.org/abs/1803.06049



**Zero-Shot Object Detection by Hybrid Region Embedding**



- arxiv: https://arxiv.org/abs/1805.06157



## OSD（One-Shot Object Detection）



**Comparison Network for One-Shot Conditional Object Detection**



- arXiv: https://arxiv.org/abs/1904.02317



**One-Shot Object Detection**



RepMet: Representative-based metric learning for classification and one-shot object detection



- intro: IBM Research AI

- arxiv:https://arxiv.org/abs/1806.04728

- github: TODO



## Weakly Supervised Object Detection



**Weakly Supervised Object Detection in Artworks**



- intro: ECCV 2018 Workshop Computer Vision for Art Analysis

- arXiv: https://arxiv.org/abs/1810.02569

- Datasets: https://wsoda.telecom-paristech.fr/downloads/dataset/IconArt_v1.zip



**Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation**



- intro: CVPR 2018

- arXiv: https://arxiv.org/abs/1803.11365

- homepage: https://naoto0804.github.io/cross_domain_detection/

- paper: http://openaccess.thecvf.com/content_cvpr_2018/html/Inoue_Cross-Domain_Weakly-Supervised_Object_CVPR_2018_paper.html

- github: https://github.com/naoto0804/cross-domain-detection



## Softer-NMS



**《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》**



- intro: CMU & Face++

- arXiv: https://arxiv.org/abs/1809.08545

- github: https://github.com/yihui-he/softer-NMS



## 2019



**Feature Selective Anchor-Free Module for Single-Shot Object Detection**



- intro: CVPR 2019



- arXiv: https://arxiv.org/abs/1903.00621



**Object Detection based on Region Decomposition and Assembly**



- intro: AAAI 2019



- arXiv: https://arxiv.org/abs/1901.08225



**Bottom-up Object Detection by Grouping Extreme and Center Points**



- intro: one stage 43.2% on COCO test-dev

- arXiv: https://arxiv.org/abs/1901.08043

- github: https://github.com/xingyizhou/ExtremeNet



**ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features**



- intro: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING



- arXiv: https://arxiv.org/abs/1901.07925



**Consistent Optimization for Single-Shot Object Detection**



- intro: improves RetinaNet from 39.1 AP to 40.1 AP on COCO datase



- arXiv: https://arxiv.org/abs/1901.06563



**Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes**



- arXiv: https://arxiv.org/abs/1901.03796



**RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free**



- arXiv: https://arxiv.org/abs/1901.03353

- github: https://github.com/chengyangfu/retinamask



**Region Proposal by Guided Anchoring**



- intro: CUHK - SenseTime Joint Lab

- arXiv: https://arxiv.org/abs/1901.03278



**Scale-Aware Trident Networks for Object Detection**



- intro: mAP of **48.4** on the COCO dataset

- arXiv: https://arxiv.org/abs/1901.01892



## 2018



**Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions**



- arXiv: https://arxiv.org/abs/1812.11901



**Strong-Weak Distribution Alignment for Adaptive Object Detection**



- arXiv: https://arxiv.org/abs/1812.04798



**AutoFocus: Efficient Multi-Scale Inference**



- intro: AutoFocus obtains an **mAP of 47.9%** (68.3% at 50% overlap) on the **COCO test-dev** set while processing **6.4 images per second on a Titan X (Pascal) GPU** 

- arXiv: https://arxiv.org/abs/1812.01600



**NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection**



- intro: Google Could

- arXiv: https://arxiv.org/abs/1812.00124



**SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection**



- intro: UC Berkeley

- arXiv: https://arxiv.org/abs/1812.00929



**Grid R-CNN**



- intro: SenseTime

- arXiv: https://arxiv.org/abs/1811.12030



**Deformable ConvNets v2: More Deformable, Better Results**



- intro: Microsoft Research Asia



- arXiv: https://arxiv.org/abs/1811.11168



**Anchor Box Optimization for Object Detection**



- intro: Microsoft Research

- arXiv: https://arxiv.org/abs/1812.00469



**Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects**



- intro: https://arxiv.org/abs/1811.12152



**NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection**



- arXiv: https://arxiv.org/abs/1812.00124



**Learning RoI Transformer for Detecting Oriented Objects in Aerial Images**



- arXiv: https://arxiv.org/abs/1812.00155



**Integrated Object Detection and Tracking with Tracklet-Conditioned Detection**



- intro: Microsoft Research Asia

- arXiv: https://arxiv.org/abs/1811.11167



**Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection**



- arXiv: https://arxiv.org/abs/1811.11318



 **Gradient Harmonized Single-stage Detector**



- intro: AAAI 2019

- arXiv: https://arxiv.org/abs/1811.05181



**CFENet: Object Detection with Comprehensive Feature Enhancement Module**



- intro: ACCV 2018

- github: https://github.com/qijiezhao/CFENet



**DeRPN: Taking a further step toward more general object detection**



- intro: AAAI 2019

- arXiv: https://arxiv.org/abs/1811.06700

- github: https://github.com/HCIILAB/DeRPN



**Hybrid Knowledge Routed Modules for Large-scale Object Detection**



- intro: Sun Yat-Sen University & Huawei Noah’s Ark Lab

- arXiv: https://arxiv.org/abs/1810.12681

- github: https://github.com/chanyn/HKRM



**《Receptive Field Block Net for Accurate and Fast Object Detection》**



- intro: ECCV 2018

- arXiv: [https://arxiv.org/abs/1711.07767](https://arxiv.org/abs/1711.07767)

- github: [https://github.com/ruinmessi/RFBNet](https://github.com/ruinmessi/RFBNet)



**Deep Feature Pyramid Reconfiguration for Object Detection**



- intro: ECCV 2018

- arXiv: https://arxiv.org/abs/1808.07993



**Unsupervised Hard Example Mining from Videos for Improved Object Detection**



- intro: ECCV 2018

- arXiv: https://arxiv.org/abs/1808.04285



**Acquisition of Localization Confidence for Accurate Object Detection**



- intro: ECCV 2018

- arXiv: https://arxiv.org/abs/1807.11590

- github: https://github.com/vacancy/PreciseRoIPooling



**Toward Scale-Invariance and Position-Sensitive Region Proposal Networks**



- intro: ECCV 2018

- arXiv: https://arxiv.org/abs/1807.09528



**MetaAnchor: Learning to Detect Objects with Customized Anchors**



- arxiv: https://arxiv.org/abs/1807.00980



**Relation Network for Object Detection**



- intro: CVPR 2018

- arxiv: https://arxiv.org/abs/1711.11575

- github:https://github.com/msracver/Relation-Networks-for-Object-Detection



**Quantization Mimic: Towards Very Tiny CNN for Object Detection**



- Tsinghua University1 & The Chinese University of Hong Kong2 &SenseTime3

- arxiv: https://arxiv.org/abs/1805.02152



**Learning Rich Features for Image Manipulation Detection**



- intro: CVPR 2018 Camera Ready

- arxiv: https://arxiv.org/abs/1805.04953



**SNIPER: Efficient Multi-Scale Training**



- arxiv:https://arxiv.org/abs/1805.09300

- github:https://github.com/mahyarnajibi/SNIPER



**Soft Sampling for Robust Object Detection**



- intro: the robustness of object detection under the presence of missing annotations

- arxiv:https://arxiv.org/abs/1806.06986



**Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria**



- intro: TNNLS 2018

- arxiv:https://arxiv.org/abs/1807.00147

- code: http://kezewang.com/codes/ASM_ver1.zip



## Other



**R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos**



- arxiv: https://arxiv.org/abs/1808.05560

- youtube: https://youtu.be/xCYD-tYudN0



# Detection Toolbox



- [Detectron(FAIR)](https://github.com/facebookresearch/Detectron): Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including [Mask R-CNN](https://arxiv.org/abs/1703.06870). It is written in Python and powered by the [Caffe2](https://github.com/caffe2/caffe2) deep learning framework.

- [Detectron2](https://github.com/facebookresearch/detectron2): Detectron2 is FAIR's next-generation research platform for object detection and segmentation.

- [maskrcnn-benchmark(FAIR)](https://github.com/facebookresearch/maskrcnn-benchmark): Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

- [mmdetection(SenseTime&CUHK)](https://github.com/open-mmlab/mmdetection): mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/).