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CVPR 2024 Papers and Open Source Project Collection (Papers with Code)

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[CVPR 2024 open source paper directory]

  • 3DGS(Gaussian Splatting)
  • Avatars
  • Backbone
  • CLIP
  • MAE
  • Embodied AI
  • GAN
  • GNN
  • Multimodal Large Language Model (MLLM)
  • Large Language Model (LLM)
  • NAS
  • OCR
  • NRF
  • DETR
  • Prompt
  • Diffusion Models
  • ReID (re-identification)
  • Long-tail distribution (Long-Tail)
  • Vision Transformer
  • Vision-Language
  • Self-supervised Learning
  • Data Augmentation
  • Object Detection
  • Anomaly Detection
  • Target tracking (Visual Tracking)
  • Semantic Segmentation
  • Instance Segmentation
  • Panoptic Segmentation
  • Medical Image
  • Medical Image Segmentation
  • Video Object Segmentation
  • Video Instance Segmentation
  • Referring Image Segmentation
  • Image Matting
  • Image Editing
  • Low-level Vision
  • Super-Resolution
  • Denoising
  • Deblur
  • Autonomous Driving
  • 3D Point Cloud
  • 3D Object Detection
  • 3D Semantic Segmentation
  • 3D Object Tracking
  • 3D Semantic Scene Completion (3D Semantic Scene Completion)
  • 3D Registration
  • 3D Human Pose Estimation
  • 3D Human Mesh Estimation
  • Medical Image
  • Image Generation
  • Video Generation
  • 3D Generation
  • Video Understanding
  • Action Detection
  • Text Detection
  • Knowledge Distillation
  • Model Pruning
  • Image Compression
  • 3D Reconstruction
  • Depth Estimation
  • Trajectory Prediction
  • Lane Detection
  • Image Captioning
  • Visual Question Answering
  • Sign Language Recognition
  • Video Prediction
  • Novel View Synthesis
  • Zero-Shot Learning (zero sample learning)
  • Stereo Matching
  • Feature Matching
  • Scene Graph Generation
  • Implicit Neural Representations
  • Image Quality Assessment
  • Video Quality Assessment
  • Datasets
  • New Tasks
  • Others

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

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

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

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

MAE

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

GAN

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

NRF

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

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

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

Prompt

Multimodal Large Language Model (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

Large Language Model (LLM)

VTimeLLM: Empower LLM to Grasp Video Moments

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

NAS

ReID (re-identification)

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

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

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

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

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

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

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

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

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

Medical Image Segmentation

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

3D point cloud (3D-Point-Cloud)

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

3D Semantic Segmentation

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

Video Editing

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers

  • Homepage: https://maskint.github.io

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

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

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

Denoising

Image Denoising

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

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

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

3D generation

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

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

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

Stereo Matching

Neural Markov Random Field for Stereo Matching

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

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

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

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

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
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