{"id":15037120,"url":"https://github.com/amusi/eccv2024-papers-with-code","last_synced_at":"2026-01-30T14:20:22.557Z","repository":{"id":43025535,"uuid":"276777288","full_name":"amusi/ECCV2024-Papers-with-Code","owner":"amusi","description":"ECCV 2024 论文和开源项目合集，同时欢迎各位大佬提交issue，分享ECCV 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ECCV 2024 论文和开源项目合集(Papers with Code)\n\nECCV 2024 decisions are now available！\n\n\n\u003e 注1：欢迎各位大佬提交issue，分享ECCV 2024论文和开源项目！\n\u003e\n\u003e 注2：关于往年CV顶会论文以及其他优质CV论文和大盘点，详见： https://github.com/amusi/daily-paper-computer-vision\n\u003e\n\u003e - [CVPR 2024](https://github.com/amusi/CVPR2024-Papers-with-Code)\n\u003e - [ECCV 2022](ECCV2022-Papers-with-Code.md)\n\u003e - [ECCV 2020](ECCV2020-Papers-with-Code.md)\n\n想看ECCV 2024和最新最全的顶会工作，欢迎扫码加入【CVer学术交流群】，这是最大的计算机视觉AI知识星球！每日更新，第一时间分享最新最前沿的计算机视觉、深度学习、自动驾驶、医疗影像和AIGC等方向的学习资料，学起来！\n\n![](CVer学术交流群.png)\n\n# 【ECCV 2024 论文开源目录】\n\n- [3DGS(Gaussian Splatting)](#3DGS)\n- [Mamba / SSM)](#Mamba)\n- [Avatars](#Avatars)\n- [Backbone](#Backbone)\n- [CLIP](#CLIP)\n- [MAE](#MAE)\n- [Embodied AI](#Embodied-AI)\n- [GAN](#GAN)\n- [GNN](#GNN)\n- [多模态大语言模型(MLLM)](#MLLM)\n- [大语言模型(LLM)](#LLM)\n- [NAS](#NAS)\n- [OCR](#OCR)\n- [NeRF](#NeRF)\n- [DETR](#DETR)\n- [Prompt](#Prompt)\n- [扩散模型(Diffusion Models)](#Diffusion)\n- [ReID(重识别)](#ReID)\n- [长尾分布(Long-Tail)](#Long-Tail)\n- [Vision Transformer](#Vision-Transformer)\n- [视觉和语言(Vision-Language)](#VL)\n- [自监督学习(Self-supervised Learning)](#SSL)\n- [数据增强(Data Augmentation)](#DA)\n- [目标检测(Object Detection)](#Object-Detection)\n- [异常检测(Anomaly Detection)](#Anomaly-Detection)\n- [目标跟踪(Visual Tracking)](#VT)\n- [语义分割(Semantic Segmentation)](#Semantic-Segmentation)\n- [实例分割(Instance Segmentation)](#Instance-Segmentation)\n- [全景分割(Panoptic Segmentation)](#Panoptic-Segmentation)\n- [医学图像(Medical Image)](#MI)\n- [医学图像分割(Medical Image Segmentation)](#MIS)\n- [视频目标分割(Video Object Segmentation)](#VOS)\n- [视频实例分割(Video Instance Segmentation)](#VIS)\n- [参考图像分割(Referring Image Segmentation)](#RIS)\n- [图像抠图(Image Matting)](#Matting)\n- [图像编辑(Image Editing)](#Image-Editing)\n- [Low-level Vision](#LLV)\n- [超分辨率(Super-Resolution)](#SR)\n- [去噪(Denoising)](#Denoising)\n- [去模糊(Deblur)](#Deblur)\n- [自动驾驶(Autonomous Driving)](#Autonomous-Driving)\n- [3D点云(3D Point Cloud)](#3D-Point-Cloud)\n- [3D目标检测(3D Object Detection)](#3DOD)\n- [3D语义分割(3D Semantic Segmentation)](#3DSS)\n- [3D目标跟踪(3D Object Tracking)](#3D-Object-Tracking)\n- [3D语义场景补全(3D Semantic Scene Completion)](#3DSSC)\n- [3D配准(3D Registration)](#3D-Registration)\n- [3D人体姿态估计(3D Human Pose Estimation)](#3D-Human-Pose-Estimation)\n- [3D人体Mesh估计(3D Human Mesh Estimation)](#3D-Human-Pose-Estimation)\n- [医学图像(Medical Image)](#Medical-Image)\n- [图像生成(Image Generation)](#Image-Generation)\n- [视频生成(Video Generation)](#Video-Generation)\n- [3D生成(3D Generation)](#3D-Generation)\n- [视频理解(Video Understanding)](#Video-Understanding)\n- [行为识别(Action Recognition)](#Action-Recognition)\n- [行为检测(Action Detection)](#Action-Detection)\n- [文本检测(Text Detection)](#Text-Detection)\n- [知识蒸馏(Knowledge Distillation)](#KD)\n- [模型剪枝(Model Pruning)](#Pruning)\n- [图像压缩(Image Compression)](#IC)\n- [三维重建(3D Reconstruction)](#3D-Reconstruction)\n- [深度估计(Depth Estimation)](#Depth-Estimation)\n- [轨迹预测(Trajectory Prediction)](#TP)\n- [车道线检测(Lane Detection)](#Lane-Detection)\n- [图像描述(Image Captioning)](#Image-Captioning)\n- [视觉问答(Visual Question Answering)](#VQA)\n- [手语识别(Sign Language Recognition)](#SLR)\n- [视频预测(Video Prediction)](#Video-Prediction)\n- [新视点合成(Novel View Synthesis)](#NVS)\n- [Zero-Shot Learning(零样本学习)](#ZSL)\n- [立体匹配(Stereo Matching)](#Stereo-Matching)\n- [特征匹配(Feature Matching)](#Feature-Matching)\n- [场景图生成(Scene Graph Generation)](#SGG)\n- [计数(Counting)](#Counting)\n- [隐式神经表示(Implicit Neural Representations)](#INR)\n- [图像质量评价(Image Quality Assessment)](#IQA)\n- [视频质量评价(Video Quality Assessment)](#Video-Quality-Assessment)\n- [数据集(Datasets)](#Datasets)\n- [新任务(New Tasks)](#New-Tasks)\n- [其他(Others)](#Others)\n\n\u003ca name=\"3DGS\"\u003e\u003c/a\u003e\n\n# 3DGS(Gaussian Splatting)\n\n**MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images**\n\n- Project: https://donydchen.github.io/mvsplat\n- Paper: https://arxiv.org/abs/2403.14627\n- Code：https://github.com/donydchen/mvsplat\n\n**CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians**\n\n- Paper: https://arxiv.org/abs/2404.01133\n- Code: https://github.com/DekuLiuTesla/CityGaussian\n\n**FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting**\n\n- Project: https://zehaozhu.github.io/FSGS/\n- Paper: https://arxiv.org/abs/2312.00451\n- Code: https://github.com/VITA-Group/FSGS\n\n\n\n\u003ca name=\"Mamba\"\u003e\u003c/a\u003e\n\n# Mamba / SSM\n\n**VideoMamba: State Space Model for Efficient Video Understanding**\n\n- Paper: https://arxiv.org/abs/2403.06977\n- Code: https://github.com/OpenGVLab/VideoMamba\n\n**ZIGMA: A DiT-style Zigzag Mamba Diffusion Model**\n\n- Paper: https://arxiv.org/abs/2403.13802\n- Code: https://taohu.me/zigma/\n\n\u003ca name=\"Avatars\"\u003e\u003c/a\u003e\n\n# Avatars\n\n\n\n\n\n\u003ca name=\"Backbone\"\u003e\u003c/a\u003e\n\n# Backbone\n\n\n\n\u003ca name=\"CLIP\"\u003e\u003c/a\u003e\n\n# CLIP\n\n\n\n\n\n\u003ca name=\"MAE\"\u003e\u003c/a\u003e\n\n# MAE\n\n\u003ca name=\"Embodied-AI\"\u003e\u003c/a\u003e\n\n# Embodied AI\n\n\n\n\u003ca name=\"GAN\"\u003e\u003c/a\u003e\n\n# GAN\n\n\u003ca name=\"OCR\"\u003e\u003c/a\u003e\n\n# OCR\n\n**Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors**\n\n- Paper: https://arxiv.org/pdf/2312.05286\n\n- Code: https://github.com/SJTU-DeepVisionLab/FreeReal \n\n**PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest Transformer**\n\n- Paper: https://arxiv.org/abs/2407.07764\n- Code: https://github.com/SJTU-DeepVisionLab/PosFormer\n\n\u003ca name=\"Occupancy\"\u003e\u003c/a\u003e\n\n# Occupancy\n\n**Fully Sparse 3D Occupancy Prediction**\n\n- Paper: https://arxiv.org/abs/2312.17118\n- Code: https://github.com/MCG-NJU/SparseOcc\n\n\n\n\u003ca name=\"NeRF\"\u003e\u003c/a\u003e\n\n# NeRF\n\n**NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields**\n\n- Project: https://nerf-mae.github.io/\n- Paper: https://arxiv.org/pdf/2404.01300\n- Code: https://github.com/zubair-irshad/NeRF-MAE \n\n\u003ca name=\"DETR\"\u003e\u003c/a\u003e\n\n# DETR\n\n\n\n\u003ca name=\"Prompt\"\u003e\u003c/a\u003e\n\n# Prompt\n\n\u003ca name=\"MLLM\"\u003e\u003c/a\u003e\n\n# 多模态大语言模型(MLLM)\n\n**SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant**\n\n- Paper: https://arxiv.org/abs/2403.11299\n- Code: https://github.com/heliossun/SQ-LLaVA\n\n**ControlCap: Controllable Region-level Captioning**\n\n- Paper: https://arxiv.org/abs/2401.17910\n- Code: https://github.com/callsys/ControlCap \n\n\u003ca name=\"LLM\"\u003e\u003c/a\u003e\n\n# 大语言模型(LLM)\n\n\n\n\u003ca name=\"NAS\"\u003e\u003c/a\u003e\n\n# NAS\n\n\u003ca name=\"ReID\"\u003e\u003c/a\u003e\n\n# ReID(重识别)\n\n\n\n\u003ca name=\"Diffusion\"\u003e\u003c/a\u003e\n\n# 扩散模型(Diffusion Models)\n\n**ZIGMA: A DiT-style Zigzag Mamba Diffusion Model**\n\n- Paper: https://arxiv.org/abs/2403.13802\n- Code: https://taohu.me/zigma/\n\n**Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation**\n\n- Paper: https://arxiv.org/abs/2403.16394\n- Code: https://github.com/zdxdsw/skewed_relations_T2I\n\n**The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization**\n\n- Project: https://ut-mao.github.io/noise.github.io/\n- Paper: https://arxiv.org/abs/2312.08872\n- Code: https://github.com/UT-Mao/Initial-Noise-Construction\n\n\u003ca name=\"Vision-Transformer\"\u003e\u003c/a\u003e\n\n# Vision Transformer\n\n**GiT: Towards Generalist Vision Transformer through Universal Language Interface**\n\n- Paper: https://arxiv.org/abs/2403.09394\n- Code: https://github.com/Haiyang-W/GiT\n\n\u003ca name=\"VL\"\u003e\u003c/a\u003e\n\n# 视觉和语言(Vision-Language)\n\n**GalLoP: Learning Global and Local Prompts for Vision-Language Models**\n\n- Paper：https://arxiv.org/abs/2407.01400\n\n\u003ca name=\"Object-Detection\"\u003e\u003c/a\u003e\n\n# 目标检测(Object Detection)\n\n**Relation DETR: Exploring Explicit Position Relation Prior for Object Detection**\n\n- Paper: https://arxiv.org/abs/2407.11699v1\n- Code: https://github.com/xiuqhou/Relation-DETR\n- Dataset: https://huggingface.co/datasets/xiuqhou/SA-Det-100k \n\n**Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector**\n\n- Project: http://yuqianfu.com/CDFSOD-benchmark/\n- Paper: https://arxiv.org/pdf/2402.03094\n- Code: https://github.com/lovelyqian/CDFSOD-benchmark \n\n\u003ca name=\"Anomaly-Detection\"\u003e\u003c/a\u003e\n\n# 异常检测(Anomaly Detection)\n\n\n\n\u003ca name=\"VT\"\u003e\u003c/a\u003e\n\n# 目标跟踪(Object Tracking)\n\n\n\n\n\n\u003ca name=\"Semantic-Segmentation\"\u003e\u003c/a\u003e\n\n# 语义分割(Semantic Segmentation)\n\n**Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation**\n\n- Paper: https://arxiv.org/abs/2405.06228\n\n- Code: https://github.com/nizhenliang/CGRSeg\n\n\u003ca name=\"MI\"\u003e\u003c/a\u003e\n\n# 医学图像(Medical Image)\n\n**Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging**\n\n- Paper: https://arxiv.org/abs/2311.16914\n- Code: https://github.com/peirong26/Brain-ID \n\n**FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification**\n\n- Project: https://ophai.hms.harvard.edu/datasets/harvard-fairdomain20k\n- Paper : https://arxiv.org/abs/2407.08813\n- Dataset: https://drive.google.com/drive/u/1/folders/1huH93JVeXMj9rK6p1OZRub868vv0UK0O\n- Code: https://github.com/Harvard-Ophthalmology-AI-Lab/FairDomain\n\n\u003ca name=\"MIS\"\u003e\u003c/a\u003e\n\n# 医学图像分割(Medical Image Segmentation)\n\n**ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image**\n\n- Project: https://scribbleprompt.csail.mit.edu/\n- Paper: https://arxiv.org/abs/2312.07381\n- Code: https://github.com/halleewong/ScribblePrompt\n\n**AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-masking**\n\n- Paper: https://arxiv.org/abs/2407.06468\n- Code: https://github.com/ricklisz/AnatoMask\n\n**Representing Topological Self-Similarity Using Fractal Feature Maps for Accurate Segmentation of Tubular Structures**\n\n- Paper: https://arxiv.org/abs/2407.14754\n- Code: https://github.com/cbmi-group/FFM-Multi-Decoder-Network \n\n\u003ca name=\"VOS\"\u003e\u003c/a\u003e\n\n# 视频目标分割(Video Object Segmentation)\n\n**DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries**\n\n- Project: https://zhang-tao-whu.github.io/projects/DVIS_DAQ/\n- Paper: https://arxiv.org/abs/2404.00086\n- Code: https://github.com/zhang-tao-whu/DVIS_Plus \n\n\u003ca name=\"Autonomous-Driving\"\u003e\u003c/a\u003e\n\n# 自动驾驶(Autonomous Driving)\n\n**Fully Sparse 3D Occupancy Prediction**\n\n- Paper: https://arxiv.org/abs/2312.17118\n- Code: https://github.com/MCG-NJU/SparseOcc\n\n**milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing**\n\n- Paper: https://arxiv.org/abs/2306.17010\n- Code: https://github.com/Toytiny/milliFlow/\n\n **4D Contrastive Superflows are Dense 3D Representation Learners**\n\n- Paper : https://arxiv.org/abs/2407.06190\n- Code: https://github.com/Xiangxu-0103/SuperFlow \n\n\u003ca name=\"3D-Point-Cloud\"\u003e\u003c/a\u003e\n\n# 3D点云(3D-Point-Cloud)\n\n\n\n\u003ca name=\"3DOD\"\u003e\u003c/a\u003e\n\n# 3D目标检测(3D Object Detection)\n\n**3D Small Object Detection with Dynamic Spatial Pruning**\n\n- Project: https://xuxw98.github.io/DSPDet3D/\n- Paper: https://arxiv.org/abs/2305.03716\n- Code: https://github.com/xuxw98/DSPDet3D\n\n**Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection**\n\n- Paper: https://arxiv.org/abs/2402.03634\n- Code: https://github.com/LiewFeng/RayDN \n\n\u003ca name=\"3DOD\"\u003e\u003c/a\u003e\n\n# 3D语义分割(3D Semantic Segmentation)\n\n\u003ca name=\"Image-Editing\"\u003e\u003c/a\u003e\n\n# 图像编辑(Image Editing)\n\n\n\n\n\n\u003ca name=\"Image-Inpainting\"\u003e\u003c/a\u003e\n\n# 图像补全/图像修复(Image Inpainting)\n\n**BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion**\n\n- Project https://tencentarc.github.io/BrushNet/\n- Paper: https://arxiv.org/abs/2403.06976\n- Code: https://github.com/TencentARC/BrushNet\n\n\u003ca name=\"Video-Editing\"\u003e\u003c/a\u003e\n\n# 视频编辑(Video Editing)\n\n\n\n\u003ca name=\"LLV\"\u003e\u003c/a\u003e\n\n# Low-level Vision\n\n**Restoring Images in Adverse Weather Conditions via Histogram Transformer**\n\n- Paper: https://arxiv.org/abs/2407.10172\n- Code: https://github.com/sunshangquan/Histoformer\n\n**OneRestore: A Universal Restoration Framework for Composite Degradation**\n\n- Project  https://gy65896.github.io/projects/ECCV2024_OneRestore\n- Paper: https://arxiv.org/abs/2407.04621\n- Code: https://github.com/gy65896/OneRestore \n\n# 超分辨率(Super-Resolution)\n\n\n\n\u003ca name=\"Denoising\"\u003e\u003c/a\u003e\n\n# 去噪(Denoising)\n\n## 图像去噪(Image Denoising)\n\n\u003ca name=\"3D-Human-Pose-Estimation\"\u003e\u003c/a\u003e\n\n# 3D人体姿态估计(3D Human Pose Estimation)\n\n\n\n\u003ca name=\"Image-Generation\"\u003e\u003c/a\u003e\n\n# 图像生成(Image Generation)\n\n**Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models**\n\n- Paper: https://arxiv.org/abs/2404.07389\n- Code: https://github.com/YasminZhang/EBAMA\n\n**Every Pixel Has its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization**\n\n- Project: https://kaminyou.com/Dense-Normalization/\n- Paper: https://arxiv.org/abs/2407.04245\n- Code: https://github.com/Kaminyou/Dense-Normalization \n\n**ZIGMA: A DiT-style Zigzag Mamba Diffusion Model**\n\n- Paper: https://arxiv.org/abs/2403.13802\n- Code: https://taohu.me/zigma/\n\n**Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation**\n\n- Paper: https://arxiv.org/abs/2403.16394\n- Code: https://github.com/zdxdsw/skewed_relations_T2I \n\n\u003ca name=\"Video-Generation\"\u003e\u003c/a\u003e\n\n# 视频生成(Video Generation)\n\n**VideoStudio: Generating Consistent-Content and Multi-Scene Videos**\n\n- Project: https://vidstudio.github.io/\n- Code: https://github.com/FuchenUSTC/VideoStudio \n\n\n\n\u003ca name=\"3D-Generation\"\u003e\u003c/a\u003e\n\n# 3D生成\n\n\n\n\u003ca name=\"Video-Understanding\"\u003e\u003c/a\u003e\n\n# 视频理解(Video Understanding)\n\n**VideoMamba: State Space Model for Efficient Video Understanding**\n\n- Paper: https://arxiv.org/abs/2403.06977\n- Code: https://github.com/OpenGVLab/VideoMamba\n\n**C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition**\n\n- Paper: https://arxiv.org/abs/2407.06113\n- Code: https://github.com/RongchangLi/ZSCAR_C2C\n\n\u003ca name=\"Action-Recognition\"\u003e\u003c/a\u003e\n\n# 行为识别(Action Recognition)\n\n**SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders**\n\n- Paper: https://arxiv.org/abs/2407.13460\n- Code: https://github.com/pha123661/SA-DVAE \n\n\u003ca name=\"KD\"\u003e\u003c/a\u003e\n\n# 知识蒸馏(Knowledge Distillation)\n\n\u003ca name=\"IC\"\u003e\u003c/a\u003e\n\n# 图像压缩(Image Compression)\n\n**Image Compression for Machine and Human Vision With Spatial-Frequency Adaptation**\n\n- Code: https://github.com/qingshi9974/ECCV2024-AdpatICMH\n- Paper: http://arxiv.org/abs/2407.09853 \n\n\u003ca name=\"Stereo-Matching\"\u003e\u003c/a\u003e\n\n# 立体匹配(Stereo Matching)\n\n\n\n\u003ca name=\"SGG\"\u003e\u003c/a\u003e\n\n# 场景图生成(Scene Graph Generation)\n\n\n\n\u003ca name=\"Counting\"\u003e\u003c/a\u003e\n\n# 计数(Counting)\n\n**Zero-shot Object Counting with Good Exemplars**\n\n- Paper: https://arxiv.org/abs/2407.04948\n- Code: https://github.com/HopooLinZ/VA-Count \n\n\n\n\u003ca name=\"Video-Quality-Assessment\"\u003e\u003c/a\u003e\n\n# 视频质量评价(Video Quality Assessment)\n\n\u003ca name=\"Datasets\"\u003e\u003c/a\u003e\n\n# 数据集(Datasets)\n\n\n\n# 其他(Others)\n\n**Multi-branch Collaborative Learning Network for 3D Visual Grounding**\n\n- Paper: https://arxiv.org/abs/2407.05363v2\n- Code: https://github.com/qzp2018/MCLN \n\n**PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers**\n\n- Code: https://github.com/ananthu-aniraj/pdiscoformer\n- Paper: https://arxiv.org/abs/2407.04538\n\n**SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments**\n\n- Project: https://fraunhoferhhi.github.io/spvloc/ \n- Paper: https://arxiv.org/abs/2404.10527\n- Code: https://github.com/fraunhoferhhi/spvloc\n\n**REFRAME: Reflective Surface Real-Time Rendering for Mobile Devices**\n\n- Project: https://xdimlab.github.io/REFRAME/\n- Paper: https://arxiv.org/abs/2403.16481\n- Code: 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