dogvane/CVPR2024-Papers-with-Code

CVPR 2024 论文和开源项目合集（标题翻译中文版）

CVPR 2024 论文和开源项目合集(Papers with Code)

CVPR 2024 decisions are now available on OpenReview！

注0：项目来自于 https://github.com/amusi/CVPR2024-Papers-with-Code， 当前项目将原文里的标题用翻译工具转为中文，未做修订，仅作参考

注1：欢迎各位大佬提交issue，分享CVPR 2024论文和开源项目！

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

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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)

3DGS(Gaussian Splatting)

Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering Scaffold-GS：结构化3D高斯函数，用于视图自适应渲染

• 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 GPS-Gaussian：可泛化的像素级3D高斯分层技术，用于实时生成人类新颖视角合成

• 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 高斯头像：通过可动3D高斯实现从单个视频中生成逼真的人类头像建模

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

GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting 高斯编辑器：利用高斯喷溅技术实现快速可控的3D编辑

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

Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction 可变形3D高斯函数用于高保真单目动态场景重建

• 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 SC-GS：用于可编辑动态场景的稀疏控制高斯喷溅

• 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 时空高斯特征喷溅技术用于实时动态视图合成

• 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 DNGaussian：通过全局-局部深度归一化优化稀疏视图3D高斯辐射场

• 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 实时动态场景渲染的4D高斯散斑技术

• 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 高斯梦者：通过连接二维和三维扩散模型实现从文本到3D高斯的快速生成

• 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 高斯头像：通过可动画的3D高斯实现从单个视频到逼真的人像建模

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

Real-Time Simulated Avatar from Head-Mounted Sensors 实时模拟头部佩戴传感器生成的虚拟形象

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

Backbone

RepViT: Revisiting Mobile CNN From ViT Perspective RepViT：从ViT视角重新审视移动CNN

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

TransNeXt: Robust Foveal Visual Perception for Vision Transformers TransNeXt：针对视觉Transformer的鲁棒性黄斑视觉感知

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

CLIP

Alpha-CLIP: A CLIP Model Focusing on Wherever You Want Alpha-CLIP：一个聚焦于您所想之处的CLIP模型

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

FairCLIP: Harnessing Fairness in Vision-Language Learning 公平CLIP：在视觉-语言学习中利用公平性

• 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 具身扫描：面向具身人工智能的全方位多模态3D感知套件

• 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 MP5：通过主动感知在Minecraft中的多模态开放式具身系统

• 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 柠檬：从二维图像中学习3D人-物交互关系

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

GAN

OCR

An Empirical Study of Scaling Law for OCR OCR缩放定律的实证研究

• 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 ODM：一种用于场景文本检测和定位的文本-图像进一步对齐预训练方法

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

NeRF

PIE-NeRF🍕: Physics-based Interactive Elastodynamics with NeRF PIE-NeRF🍕：基于物理的交互式弹性动力学与NeRF

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

DETR

DETRs Beat YOLOs on Real-time Object Detection DETR在实时目标检测上击败了YOLOs

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

Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement 显著性DETR：通过层次显著性过滤精炼增强检测Transformer

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

Prompt

多模态大语言模型(MLLM)

mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration mPLUG-Owl2：通过模态协作革新多模态大型语言模型

• 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 多模态LLM的链接上下文学习

• 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 OPERA：通过过度信任惩罚和反思-分配缓解多模态大型语言模型中的幻觉

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

Making Large Multimodal Models Understand Arbitrary Visual Prompts 制作能够理解任意视觉提示的大型多模态模型

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

Pink: Unveiling the power of referential comprehension for multi-modal llms 粉红色：揭示多模态LLMs中参照理解的力量

• 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 Chat-UniVi：统一视觉表示通过图像和视频理解赋能大型语言模型

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

OneLLM: One Framework to Align All Modalities with Language OneLLM：一个框架，将所有模态与语言对齐

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

大语言模型(LLM)

VTimeLLM: Empower LLM to Grasp Video Moments VTimeLLM：赋予LLM把握视频瞬间的能力

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

NAS

ReID(重识别)

Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification 魔法令牌：为多模态物体重识别选择多样化的令牌

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

Noisy-Correspondence Learning for Text-to-Image Person Re-identification 文本到图像人物重识别的噪声对应学习

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

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

扩散模型(Diffusion Models)

InstanceDiffusion: Instance-level Control for Image Generation 实例扩散：图像生成中的实例级控制

• 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 残差去噪扩散模型

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

DeepCache: Accelerating Diffusion Models for Free DeepCache：免费加速扩散模型

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

DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations DEADiff：一种具有解耦表示的高效风格扩散模型

• 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 SVGDreamer：基于扩散模型的文本引导SVG生成

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

InteractDiffusion: Interaction-Control for Text-to-Image Diffusion Model 交互式扩散：文本到图像扩散模型的交互控制

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

MMA-Diffusion: MultiModal Attack on Diffusion Models MMA-Diffusion：对扩散模型的跨模态攻击

• 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 视频运动定制：利用时间注意力自适应的文本到视频扩散模型

• 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 TransNeXt：为视觉Transformer提供鲁棒的黄斑视觉感知

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

RepViT: Revisiting Mobile CNN From ViT Perspective RepViT：从ViT视角重新审视移动CNN

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

A General and Efficient Training for Transformer via Token Expansion 通过词元扩展进行通用且高效的Transformer训练

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

视觉和语言(Vision-Language)

PromptKD: Unsupervised Prompt Distillation for Vision-Language Models 提示KD：用于视觉-语言模型的无监督提示蒸馏

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

FairCLIP: Harnessing Fairness in Vision-Language Learning 公平CLIP：在视觉语言学习中利用公平性

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

目标检测(Object Detection)

DETRs Beat YOLOs on Real-time Object Detection DETRs在实时目标检测方面击败了YOLOs

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

Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation 利用零样本日夜间域适应增强目标检测

• 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 YOLO-World：实时开放词汇物体检测

• 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 显著性DETR：通过分层显著性滤波优化提升检测Transformer

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

异常检测(Anomaly Detection)

Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection 开放集监督异常检测中的异常异质性学习

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

目标跟踪(Object Tracking)

Delving into the Trajectory Long-tail Distribution for Muti-object Tracking 深入探究多目标跟踪中的轨迹长尾分布

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

语义分割(Semantic Segmentation)

Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation 更强、更少、更优越：利用视觉基础模型实现领域泛化语义分割

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

SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation 开放词汇语义分割的简单编码器-解码器：SED

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

医学图像(Medical Image)

Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology 特征再嵌入：迈向计算病理学基础模型级别的性能

• 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 VoCo：一种简单而有效的3D医学图像分析体积对比学习框架

• 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 ChAda-ViT：异构显微镜图像联合表示学习的通道自适应注意力

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

医学图像分割(Medical Image Segmentation)

自动驾驶(Autonomous Driving)

UniPAD: A Universal Pre-training Paradigm for Autonomous Driving UniPAD：自动驾驶的通用预训练范式

• 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 Cam4DOcc：自动驾驶应用中仅使用摄像头进行4D占用预测的基准测试

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

Memory-based Adapters for Online 3D Scene Perception 基于内存的在线3D场景感知适配器

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

Symphonize 3D Semantic Scene Completion with Contextual Instance Queries 将3D语义场景补全与上下文实例查询同步化

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

A Real-world Large-scale Dataset for Roadside Cooperative Perception 真实世界大规模道路侧协同感知数据集

• 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 单视和多视深度自适应融合用于自动驾驶

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

Traffic Scene Parsing through the TSP6K Dataset 通过TSP6K数据集进行交通场景解析

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

3D点云(3D-Point-Cloud)

3D目标检测(3D Object Detection)

PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection PTT：高效时序3D目标检测的点-轨迹变换器

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

UniMODE: Unified Monocular 3D Object Detection UniMODE：统一单目3D目标检测

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

3D语义分割(3D Semantic Segmentation)

图像编辑(Image Editing)

Edit One for All: Interactive Batch Image Editing 一键编辑：交互式批量图像编辑

• 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

视频编辑(Video Editing)

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers MaskINT：通过插值非自回归掩码变换器进行视频编辑

• Homepage: https://maskint.github.io

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

Low-level Vision

Residual Denoising Diffusion Models 残差去噪扩散模型

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

Boosting Image Restoration via Priors from Pre-trained Models 通过预训练模型先验信息增强图像恢复

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

超分辨率(Super-Resolution)

SeD: Semantic-Aware Discriminator for Image Super-Resolution SeD：图像超分辨率中的语义感知判别器

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

APISR: Anime Production Inspired Real-World Anime Super-Resolution APISR：受动画制作启发的现实世界动画超分辨率

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

去噪(Denoising)

图像去噪(Image Denoising)

3D人体姿态估计(3D Human Pose Estimation)

Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation 沙漏分词器用于高效基于Transformer的3D人体姿态估计

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

图像生成(Image Generation)

InstanceDiffusion: Instance-level Control for Image Generation 实例扩散：图像生成中的实例级控制

• 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 ECLIPSE：一种高效利用资源的文本到图像生成先验

• 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 指令-图像：多模态指令下的图像生成

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

Residual Denoising Diffusion Models 残差去噪扩散模型

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

UniGS: Unified Representation for Image Generation and Segmentation UniGS：图像生成与分割的统一表示

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

Multi-Instance Generation Controller for Text-to-Image Synthesis 多实例生成控制器，用于文本到图像合成

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

SVGDreamer: Text Guided SVG Generation with Diffusion Model SVGDreamer：基于扩散模型的文本引导SVG生成

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

InteractDiffusion: Interaction-Control for Text-to-Image Diffusion Model 交互扩散：文本到图像扩散模型的交互控制

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

Ranni: Taming Text-to-Image Diffusion for Accurate Prompt Following Ranni：驯服文本到图像扩散，实现准确提示跟随

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

视频生成(Video Generation)

Vlogger: Make Your Dream A Vlog 视频博主：让你的梦想成为一档视频博客

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

VBench: Comprehensive Benchmark Suite for Video Generative Models VBench：视频生成模型的全面基准测试套件

• 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 视频运动定制：利用时间注意力自适应的文本到视频扩散模型

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

3D生成

CityDreamer: Compositional Generative Model of Unbounded 3D Cities 城市梦想家：无限3D城市的构图生成模型

• 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 清醒梦境者：通过区间得分匹配实现高保真文本到3D生成

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

视频理解(Video Understanding)

MVBench: A Comprehensive Multi-modal Video Understanding Benchmark MVBench：一个全面的跨模态视频理解基准

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

知识蒸馏(Knowledge Distillation)

Logit Standardization in Knowledge Distillation 知识蒸馏中的Logit标准化

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

Efficient Dataset Distillation via Minimax Diffusion 通过最小-最大扩散进行高效数据集蒸馏

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

立体匹配(Stereo Matching)

Neural Markov Random Field for Stereo Matching 神经马尔可夫随机场用于立体匹配

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

场景图生成(Scene Graph Generation)

HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation HiKER-SGG：层次知识增强鲁棒场景图生成

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

视频质量评价(Video Quality Assessment)

KVQ: Kaleidoscope Video Quality Assessment for Short-form Videos KVQ：短视频的万花筒视频质量评估

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

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

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

数据集(Datasets)

A Real-world Large-scale Dataset for Roadside Cooperative Perception 现实世界大规模道路侧协同感知数据集

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

Traffic Scene Parsing through the TSP6K Dataset 通过TSP6K数据集进行交通场景解析

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

其他(Others)

Object Recognition as Next Token Prediction 对象识别作为下一个标记预测

• 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 ParameterNet：参数即是所有，用于移动网络大规模视觉预训练

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

Seamless Human Motion Composition with Blended Positional Encodings 无缝的人体运动合成与混合位置编码

• 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 LL3DA：用于全3D理解、推理和规划的视觉交互式指令调优

• 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 MoMask：3D人体动作的生成式掩码建模

• 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 通过自洽解释提升视觉定位

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

ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object ImageNet-D：在扩散合成物体上基准测试神经网络鲁棒性

• 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 从合成人类群体活动中学习

• 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 跨学科大脑解码框架

• 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 通过低秩专家混合的多任务密集预测

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

Contrastive Mean-Shift Learning for Generalized Category Discovery 对比均值漂移学习用于广义类别发现

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