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CVPR 2024 论文和开源项目合集(Papers with Code)

CVPR 2024 decisions are now available on OpenReview！

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• ECCV 2024
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【CVPR 2024 论文开源目录】

• 3DGS(Gaussian Splatting)
• Avatars
• Backbone
• CLIP
• MAE
• Embodied AI
• GAN
• GNN
• 多模态大语言模型(MLLM)
• 大语言模型(LLM)
• NAS
• OCR
• NeRF
• DETR
• Prompt
• 扩散模型(Diffusion Models)
• ReID(重识别)
• 长尾分布(Long-Tail)
• Vision Transformer
• 视觉和语言(Vision-Language)
• 自监督学习(Self-supervised Learning)
• 数据增强(Data Augmentation)
• 目标检测(Object Detection)
• 异常检测(Anomaly Detection)
• 目标跟踪(Visual Tracking)
• 语义分割(Semantic Segmentation)
• 实例分割(Instance Segmentation)
• 全景分割(Panoptic Segmentation)
• 医学图像(Medical Image)
• 医学图像分割(Medical Image Segmentation)
• 视频目标分割(Video Object Segmentation)
• 视频实例分割(Video Instance Segmentation)
• 参考图像分割(Referring Image Segmentation)
• 图像抠图(Image Matting)
• 图像编辑(Image Editing)
• Low-level Vision
• 超分辨率(Super-Resolution)
• 去噪(Denoising)
• 去模糊(Deblur)
• 自动驾驶(Autonomous Driving)
• 3D点云(3D Point Cloud)
• 3D目标检测(3D Object Detection)
• 3D语义分割(3D Semantic Segmentation)
• 3D目标跟踪(3D Object Tracking)
• 3D语义场景补全(3D Semantic Scene Completion)
• 3D配准(3D Registration)
• 3D人体姿态估计(3D Human Pose Estimation)
• 3D人体Mesh估计(3D Human Mesh Estimation)
• 医学图像(Medical Image)
• 图像生成(Image Generation)
• 视频生成(Video Generation)
• 3D生成(3D Generation)
• 视频理解(Video Understanding)
• 行为检测(Action Detection)
• 文本检测(Text Detection)
• 知识蒸馏(Knowledge Distillation)
• 模型剪枝(Model Pruning)
• 图像压缩(Image Compression)
• 三维重建(3D Reconstruction)
• 深度估计(Depth Estimation)
• 轨迹预测(Trajectory Prediction)
• 车道线检测(Lane Detection)
• 图像描述(Image Captioning)
• 视觉问答(Visual Question Answering)
• 手语识别(Sign Language Recognition)
• 视频预测(Video Prediction)
• 新视点合成(Novel View Synthesis)
• Zero-Shot Learning(零样本学习)
• 立体匹配(Stereo Matching)
• 特征匹配(Feature Matching)
• 场景图生成(Scene Graph Generation)
• 隐式神经表示(Implicit Neural Representations)
• 图像质量评价(Image Quality Assessment)
• 视频质量评价(Video Quality Assessment)
• 数据集(Datasets)
• 新任务(New Tasks)
• 其他(Others)

<a name="3DGS"></a>

3DGS(Gaussian Splatting)

Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering

• Homepage: https://city-super.github.io/scaffold-gs/
• Paper: https://arxiv.org/abs/2312.00109
• Code: https://github.com/city-super/Scaffold-GS

GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis

• Homepage: https://shunyuanzheng.github.io/GPS-Gaussian
• Paper: https://arxiv.org/abs/2312.02155
• Code: https://github.com/ShunyuanZheng/GPS-Gaussian

GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians

• Paper: https://arxiv.org/abs/2312.02134
• Code: https://github.com/huliangxiao/GaussianAvatar

GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting

• Paper: https://arxiv.org/abs/2311.14521
• Code: https://github.com/buaacyw/GaussianEditor

Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction

• Homepage: https://ingra14m.github.io/Deformable-Gaussians/
• Paper: https://arxiv.org/abs/2309.13101
• Code: https://github.com/ingra14m/Deformable-3D-Gaussians

SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes

• Homepage: https://yihua7.github.io/SC-GS-web/
• Paper: https://arxiv.org/abs/2312.14937
• Code: https://github.com/yihua7/SC-GS

Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis

• Homepage: https://oppo-us-research.github.io/SpacetimeGaussians-website/
• Paper: https://arxiv.org/abs/2312.16812
• Code: https://github.com/oppo-us-research/SpacetimeGaussians

DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization

• Homepage: https://fictionarry.github.io/DNGaussian/
• Paper: https://arxiv.org/abs/2403.06912
• Code: https://github.com/Fictionarry/DNGaussian

4D Gaussian Splatting for Real-Time Dynamic Scene Rendering

• Paper: https://arxiv.org/abs/2310.08528
• Code: https://github.com/hustvl/4DGaussians

GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models

• Paper: https://arxiv.org/abs/2310.08529
• Code: https://github.com/hustvl/GaussianDreamer

<a name="Avatars"></a>

Avatars

GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians

• Paper: https://arxiv.org/abs/2312.02134
• Code: https://github.com/huliangxiao/GaussianAvatar

Real-Time Simulated Avatar from Head-Mounted Sensors

• Homepage: https://www.zhengyiluo.com/SimXR/
• Paper: https://arxiv.org/abs/2403.06862

<a name="Backbone"></a>

Backbone

RepViT: Revisiting Mobile CNN From ViT Perspective

• Paper: https://arxiv.org/abs/2307.09283
• Code: https://github.com/THU-MIG/RepViT

TransNeXt: Robust Foveal Visual Perception for Vision Transformers

• Paper: https://arxiv.org/abs/2311.17132
• Code: https://github.com/DaiShiResearch/TransNeXt

<a name="CLIP"></a>

CLIP

Alpha-CLIP: A CLIP Model Focusing on Wherever You Want

• Paper: https://arxiv.org/abs/2312.03818
• Code: https://github.com/SunzeY/AlphaCLIP

FairCLIP: Harnessing Fairness in Vision-Language Learning

• Paper: https://arxiv.org/abs/2403.19949
• Code: https://github.com/Harvard-Ophthalmology-AI-Lab/FairCLIP

<a name="MAE"></a>

MAE

<a name="Embodied-AI"></a>

Embodied AI

EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI

• Homepage: https://tai-wang.github.io/embodiedscan/
• Paper: https://arxiv.org/abs/2312.16170
• Code: https://github.com/OpenRobotLab/EmbodiedScan

MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception

• Homepage: https://iranqin.github.io/MP5.github.io/
• Paper: https://arxiv.org/abs/2312.07472
• Code: https://github.com/IranQin/MP5

LEMON: Learning 3D Human-Object Interaction Relation from 2D Images

• Paper: https://arxiv.org/abs/2312.08963
• Code: https://github.com/yyvhang/lemon_3d

<a name="GAN"></a>

GAN

<a name="OCR"></a>

OCR

An Empirical Study of Scaling Law for OCR

• Paper: https://arxiv.org/abs/2401.00028
• Code: https://github.com/large-ocr-model/large-ocr-model.github.io

ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting

• Paper: https://arxiv.org/abs/2403.00303
• Code: https://github.com/PriNing/ODM

<a name="NeRF"></a>

NeRF

PIE-NeRF🍕: Physics-based Interactive Elastodynamics with NeRF

• Paper: https://arxiv.org/abs/2311.13099
• Code: https://github.com/FYTalon/pienerf/

<a name="DETR"></a>

DETR

DETRs Beat YOLOs on Real-time Object Detection

• Paper: https://arxiv.org/abs/2304.08069
• Code: https://github.com/lyuwenyu/RT-DETR

Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement

• Paper: https://arxiv.org/abs/2403.16131
• Code: https://github.com/xiuqhou/Salience-DETR

<a name="Prompt"></a>

Prompt

<a name="MLLM"></a>

多模态大语言模型(MLLM)

mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration

• Paper: https://arxiv.org/abs/2311.04257
• Code: https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2

Link-Context Learning for Multimodal LLMs

• Paper: https://arxiv.org/abs/2308.07891
• Code: https://github.com/isekai-portal/Link-Context-Learning/tree/main

OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

• Paper: https://arxiv.org/abs/2311.17911
• Code: https://github.com/shikiw/OPERA

Making Large Multimodal Models Understand Arbitrary Visual Prompts

• Homepage: https://vip-llava.github.io/
• Paper: https://arxiv.org/abs/2312.00784

Pink: Unveiling the power of referential comprehension for multi-modal llms

• Paper: https://arxiv.org/abs/2310.00582
• Code: https://github.com/SY-Xuan/Pink

Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

• Paper: https://arxiv.org/abs/2311.08046
• Code: https://github.com/PKU-YuanGroup/Chat-UniVi

OneLLM: One Framework to Align All Modalities with Language

• Paper: https://arxiv.org/abs/2312.03700
• Code: https://github.com/csuhan/OneLLM

<a name="LLM"></a>

大语言模型(LLM)

VTimeLLM: Empower LLM to Grasp Video Moments

• Paper: https://arxiv.org/abs/2311.18445
• Code: https://github.com/huangb23/VTimeLLM

<a name="NAS"></a>

NAS

<a name="ReID"></a>

ReID(重识别)

Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification

• Paper: https://arxiv.org/abs/2403.10254
• Code: https://github.com/924973292/EDITOR

Noisy-Correspondence Learning for Text-to-Image Person Re-identification

• Paper: https://arxiv.org/abs/2308.09911

• Code : https://github.com/QinYang79/RDE

<a name="Diffusion"></a>

扩散模型(Diffusion Models)

InstanceDiffusion: Instance-level Control for Image Generation

• Homepage: https://people.eecs.berkeley.edu/~xdwang/projects/InstDiff/

• Paper: https://arxiv.org/abs/2402.03290

• Code: https://github.com/frank-xwang/InstanceDiffusion

Residual Denoising Diffusion Models

• Paper: https://arxiv.org/abs/2308.13712
• Code: https://github.com/nachifur/RDDM

DeepCache: Accelerating Diffusion Models for Free

• Paper: https://arxiv.org/abs/2312.00858
• Code: https://github.com/horseee/DeepCache

DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations

• Homepage: https://tianhao-qi.github.io/DEADiff/

• Paper: https://arxiv.org/abs/2403.06951

• Code: https://github.com/Tianhao-Qi/DEADiff_code

SVGDreamer: Text Guided SVG Generation with Diffusion Model

• Paper: https://arxiv.org/abs/2312.16476
• Code: https://ximinng.github.io/SVGDreamer-project/

InteractDiffusion: Interaction-Control for Text-to-Image Diffusion Model

• Paper: https://arxiv.org/abs/2312.05849
• Code: https://github.com/jiuntian/interactdiffusion

MMA-Diffusion: MultiModal Attack on Diffusion Models

• Paper: https://arxiv.org/abs/2311.17516
• Code: https://github.com/yangyijune/MMA-Diffusion

VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models

• Homeoage: https://video-motion-customization.github.io/
• Paper: https://arxiv.org/abs/2312.00845
• Code: https://github.com/HyeonHo99/Video-Motion-Customization

<a name="Vision-Transformer"></a>

Vision Transformer

TransNeXt: Robust Foveal Visual Perception for Vision Transformers

• Paper: https://arxiv.org/abs/2311.17132
• Code: https://github.com/DaiShiResearch/TransNeXt

RepViT: Revisiting Mobile CNN From ViT Perspective

• Paper: https://arxiv.org/abs/2307.09283
• Code: https://github.com/THU-MIG/RepViT

A General and Efficient Training for Transformer via Token Expansion

• Paper: https://arxiv.org/abs/2404.00672
• Code: https://github.com/Osilly/TokenExpansion

<a name="VL"></a>

视觉和语言(Vision-Language)

PromptKD: Unsupervised Prompt Distillation for Vision-Language Models

• Paper: https://arxiv.org/abs/2403.02781
• Code: https://github.com/zhengli97/PromptKD

FairCLIP: Harnessing Fairness in Vision-Language Learning

• Paper: https://arxiv.org/abs/2403.19949
• Code: https://github.com/Harvard-Ophthalmology-AI-Lab/FairCLIP

<a name="Object-Detection"></a>

目标检测(Object Detection)

DETRs Beat YOLOs on Real-time Object Detection

• Paper: https://arxiv.org/abs/2304.08069
• Code: https://github.com/lyuwenyu/RT-DETR

Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation

• Paper: https://arxiv.org/abs/2312.01220
• Code: https://github.com/ZPDu/Boosting-Object-Detection-with-Zero-Shot-Day-Night-Domain-Adaptation

YOLO-World: Real-Time Open-Vocabulary Object Detection

• Paper: https://arxiv.org/abs/2401.17270
• Code: https://github.com/AILab-CVC/YOLO-World

Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement

• Paper: https://arxiv.org/abs/2403.16131
• Code: https://github.com/xiuqhou/Salience-DETR

<a name="Anomaly-Detection"></a>

异常检测(Anomaly Detection)

Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection

• Paper: https://arxiv.org/abs/2310.12790
• Code: https://github.com/mala-lab/AHL

<a name="VT"></a>

目标跟踪(Object Tracking)

Delving into the Trajectory Long-tail Distribution for Muti-object Tracking

• Paper: https://arxiv.org/abs/2403.04700
• Code: https://github.com/chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT

<a name="Semantic-Segmentation"></a>

语义分割(Semantic Segmentation)

Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation

• Paper: https://arxiv.org/abs/2312.04265
• Code: https://github.com/w1oves/Rein

SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation

• Paper: https://arxiv.org/abs/2311.15537
• Code: https://github.com/xb534/SED

<a name="MI"></a>

医学图像(Medical Image)

Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology

• Paper: https://arxiv.org/abs/2402.17228
• Code: https://github.com/DearCaat/RRT-MIL

VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis

• Paper: https://arxiv.org/abs/2402.17300
• Code: https://github.com/Luffy03/VoCo

ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images

• Paper: https://arxiv.org/abs/2311.15264
• Code: https://github.com/nicoboou/chada_vit

<a name="MIS"></a>

医学图像分割(Medical Image Segmentation)

<a name="Autonomous-Driving"></a>

自动驾驶(Autonomous Driving)

UniPAD: A Universal Pre-training Paradigm for Autonomous Driving

• Paper: https://arxiv.org/abs/2310.08370
• Code: https://github.com/Nightmare-n/UniPAD

Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications

• Paper: https://arxiv.org/abs/2311.17663
• Code: https://github.com/haomo-ai/Cam4DOcc

Memory-based Adapters for Online 3D Scene Perception

• Paper: https://arxiv.org/abs/2403.06974
• Code: https://github.com/xuxw98/Online3D

Symphonize 3D Semantic Scene Completion with Contextual Instance Queries

• Paper: https://arxiv.org/abs/2306.15670
• Code: https://github.com/hustvl/Symphonies

A Real-world Large-scale Dataset for Roadside Cooperative Perception

• Paper: https://arxiv.org/abs/2403.10145
• Code: https://github.com/AIR-THU/DAIR-RCooper

Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving

• Paper: https://arxiv.org/abs/2403.07535
• Code: https://github.com/Junda24/AFNet

Traffic Scene Parsing through the TSP6K Dataset

• Paper: https://arxiv.org/pdf/2303.02835.pdf
• Code: https://github.com/PengtaoJiang/TSP6K

<a name="3D-Point-Cloud"></a>

3D点云(3D-Point-Cloud)

<a name="3DOD"></a>

3D目标检测(3D Object Detection)

PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection

• Paper: https://arxiv.org/abs/2312.08371
• Code: https://github.com/kuanchihhuang/PTT

UniMODE: Unified Monocular 3D Object Detection

• Paper: https://arxiv.org/abs/2402.18573

<a name="3DOD"></a>

3D语义分割(3D Semantic Segmentation)

<a name="Image-Editing"></a>

图像编辑(Image Editing)

Edit One for All: Interactive Batch Image Editing

• Homepage: https://thaoshibe.github.io/edit-one-for-all
• Paper: https://arxiv.org/abs/2401.10219
• Code: https://github.com/thaoshibe/edit-one-for-all

<a name="Video-Editing"></a>

视频编辑(Video Editing)

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers

• Homepage: https://maskint.github.io

• Paper: https://arxiv.org/abs/2312.12468

<a name="LLV"></a>

Low-level Vision

Residual Denoising Diffusion Models

• Paper: https://arxiv.org/abs/2308.13712
• Code: https://github.com/nachifur/RDDM

Boosting Image Restoration via Priors from Pre-trained Models

• Paper: https://arxiv.org/abs/2403.06793

<a name="SR"></a>

超分辨率(Super-Resolution)

SeD: Semantic-Aware Discriminator for Image Super-Resolution

• Paper: https://arxiv.org/abs/2402.19387
• Code: https://github.com/lbc12345/SeD

APISR: Anime Production Inspired Real-World Anime Super-Resolution

• Paper: https://arxiv.org/abs/2403.01598
• Code: https://github.com/Kiteretsu77/APISR

<a name="Denoising"></a>

去噪(Denoising)

图像去噪(Image Denoising)

<a name="3D-Human-Pose-Estimation"></a>

3D人体姿态估计(3D Human Pose Estimation)

Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation

• Paper: https://arxiv.org/abs/2311.12028
• Code: https://github.com/NationalGAILab/HoT

<a name="Image-Generation"></a>

图像生成(Image Generation)

InstanceDiffusion: Instance-level Control for Image Generation

• Homepage: https://people.eecs.berkeley.edu/~xdwang/projects/InstDiff/

• Paper: https://arxiv.org/abs/2402.03290

• Code: https://github.com/frank-xwang/InstanceDiffusion

ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations

• Homepage: https://eclipse-t2i.vercel.app/

• Paper: https://arxiv.org/abs/2312.04655

• Code: https://github.com/eclipse-t2i/eclipse-inference

Instruct-Imagen: Image Generation with Multi-modal Instruction

• Paper: https://arxiv.org/abs/2401.01952

Residual Denoising Diffusion Models

• Paper: https://arxiv.org/abs/2308.13712
• Code: https://github.com/nachifur/RDDM

UniGS: Unified Representation for Image Generation and Segmentation

• Paper: https://arxiv.org/abs/2312.01985

Multi-Instance Generation Controller for Text-to-Image Synthesis

• Paper: https://arxiv.org/abs/2402.05408
• Code: https://github.com/limuloo/migc

SVGDreamer: Text Guided SVG Generation with Diffusion Model

• Paper: https://arxiv.org/abs/2312.16476
• Code: https://ximinng.github.io/SVGDreamer-project/

InteractDiffusion: Interaction-Control for Text-to-Image Diffusion Model

• Paper: https://arxiv.org/abs/2312.05849
• Code: https://github.com/jiuntian/interactdiffusion

Ranni: Taming Text-to-Image Diffusion for Accurate Prompt Following

• Paper: https://arxiv.org/abs/2311.17002
• Code: https://github.com/ali-vilab/Ranni

<a name="Video-Generation"></a>

视频生成(Video Generation)

Vlogger: Make Your Dream A Vlog

• Paper: https://arxiv.org/abs/2401.09414
• Code: https://github.com/Vchitect/Vlogger

VBench: Comprehensive Benchmark Suite for Video Generative Models

• Homepage: https://vchitect.github.io/VBench-project/
• Paper: https://arxiv.org/abs/2311.17982
• Code: https://github.com/Vchitect/VBench

VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models

• Homeoage: https://video-motion-customization.github.io/
• Paper: https://arxiv.org/abs/2312.00845
• Code: https://github.com/HyeonHo99/Video-Motion-Customization

<a name="3D-Generation"></a>

3D生成

CityDreamer: Compositional Generative Model of Unbounded 3D Cities

• Homepage: https://haozhexie.com/project/city-dreamer/
• Paper: https://arxiv.org/abs/2309.00610
• Code: https://github.com/hzxie/city-dreamer

LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching

• Paper: https://arxiv.org/abs/2311.11284
• Code: https://github.com/EnVision-Research/LucidDreamer

<a name="Video-Understanding"></a>

视频理解(Video Understanding)

MVBench: A Comprehensive Multi-modal Video Understanding Benchmark

• Paper: https://arxiv.org/abs/2311.17005
• Code: https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2

<a name="KD"></a>

知识蒸馏(Knowledge Distillation)

Logit Standardization in Knowledge Distillation

• Paper: https://arxiv.org/abs/2403.01427
• Code: https://github.com/sunshangquan/logit-standardization-KD

Efficient Dataset Distillation via Minimax Diffusion

• Paper: https://arxiv.org/abs/2311.15529
• Code: https://github.com/vimar-gu/MinimaxDiffusion

<a name="Stereo-Matching"></a>

立体匹配(Stereo Matching)

Neural Markov Random Field for Stereo Matching

• Paper: https://arxiv.org/abs/2403.11193
• Code: https://github.com/aeolusguan/NMRF

<a name="SGG"></a>

场景图生成(Scene Graph Generation)

HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation

• Homepage: https://zhangce01.github.io/HiKER-SGG/
• Paper : https://arxiv.org/abs/2403.12033
• Code: https://github.com/zhangce01/HiKER-SGG

<a name="Video-Quality-Assessment"></a>

视频质量评价(Video Quality Assessment)

KVQ: Kaleidoscope Video Quality Assessment for Short-form Videos

• Homepage: https://lixinustc.github.io/projects/KVQ/

• Paper: https://arxiv.org/abs/2402.07220

• Code: https://github.com/lixinustc/KVQ-Challenge-CVPR-NTIRE2024

<a name="Datasets"></a>

数据集(Datasets)

A Real-world Large-scale Dataset for Roadside Cooperative Perception

• Paper: https://arxiv.org/abs/2403.10145
• Code: https://github.com/AIR-THU/DAIR-RCooper

Traffic Scene Parsing through the TSP6K Dataset

• Paper: https://arxiv.org/pdf/2303.02835.pdf
• Code: https://github.com/PengtaoJiang/TSP6K

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其他(Others)

Object Recognition as Next Token Prediction

• Paper: https://arxiv.org/abs/2312.02142
• Code: https://github.com/kaiyuyue/nxtp

ParameterNet: Parameters Are All You Need for Large-scale Visual Pretraining of Mobile Networks

• Paper: https://arxiv.org/abs/2306.14525
• Code: https://parameternet.github.io/

Seamless Human Motion Composition with Blended Positional Encodings

• Paper: https://arxiv.org/abs/2402.15509
• Code: https://github.com/BarqueroGerman/FlowMDM

LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning

• Homepage: https://ll3da.github.io/

• Paper: https://arxiv.org/abs/2311.18651

• Code: https://github.com/Open3DA/LL3DA

CLOVA: A Closed-LOop Visual Assistant with Tool Usage and Update

• Homepage: https://clova-tool.github.io/
• Paper: https://arxiv.org/abs/2312.10908

MoMask: Generative Masked Modeling of 3D Human Motions

• Paper: https://arxiv.org/abs/2312.00063
• Code: https://github.com/EricGuo5513/momask-codes

Amodal Ground Truth and Completion in the Wild

• Homepage: https://www.robots.ox.ac.uk/~vgg/research/amodal/
• Paper: https://arxiv.org/abs/2312.17247
• Code: https://github.com/Championchess/Amodal-Completion-in-the-Wild

Improved Visual Grounding through Self-Consistent Explanations

• Paper: https://arxiv.org/abs/2312.04554
• Code: https://github.com/uvavision/SelfEQ

ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object

• Homepage: https://chenshuang-zhang.github.io/imagenet_d/
• Paper: https://arxiv.org/abs/2403.18775
• Code: https://github.com/chenshuang-zhang/imagenet_d

Learning from Synthetic Human Group Activities

• Homepage: https://cjerry1243.github.io/M3Act/
• Paper https://arxiv.org/abs/2306.16772
• Code: https://github.com/cjerry1243/M3Act

A Cross-Subject Brain Decoding Framework

• Homepage: https://littlepure2333.github.io/MindBridge/
• Paper: https://arxiv.org/abs/2404.07850
• Code: https://github.com/littlepure2333/MindBridge

Multi-Task Dense Prediction via Mixture of Low-Rank Experts

• Paper : https://arxiv.org/abs/2403.17749
• Code: https://github.com/YuqiYang213/MLoRE

Contrastive Mean-Shift Learning for Generalized Category Discovery

• Homepage: https://postech-cvlab.github.io/cms/
• Paper: https://arxiv.org/abs/2404.09451
• Code: https://github.com/sua-choi/CMS