nigelnew / cvpr2023-papers-with-code

CVPR 2023 论文和开源项目合集

CVPR 2023 论文和开源项目合集(Papers with Code)

CVPR 2023 论文和开源项目合集(papers with code)！

25.78% = 2360 / 9155

CVPR2023 decisions are now available on OpenReview! This year, wereceived a record number of 9155 submissions (a 12% increase over CVPR2022), and accepted 2360 papers, for a 25.78% acceptance rate.

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【CVPR 2023 论文开源目录】

• Backbone
• CLIP
• MAE
• GAN
• GNN
• MLP
• NAS
• OCR
• NeRF
• DETR
• Diffusion Models(扩散模型)
• Avatars
• 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)
• 目标跟踪(Visual Tracking)
• 语义分割(Semantic Segmentation)
• 实例分割(Instance Segmentation)
• 全景分割(Panoptic Segmentation)
• 参考图像分割(Referring Image Segmentation )
• 图像抠图(Image Matting)
• 视频理解(Video Understanding)
• 图像编辑(Image Editing)
• Low-level Vision
• 超分辨率(Super-Resolution)
• 去模糊(Deblur)
• 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人体姿态估计(3D Human Pose Estimation)
• 3D语义场景补全(3D Semantic Scene Completion)
• 医学图像(Medical Image)
• 图像生成(Image Generation)
• 视频生成(Video Generation)
• 知识蒸馏(Knowledge Distillation)
• 三维重建(3D Reconstruction)
• 深度估计(Depth Estimation)
• 轨迹预测(Trajectory Prediction)
• 视觉问答(Visual Question Answering)
• 手语识别(Sign Language Recognition)
• 视频预测(Video Prediction)
• 数据集(Datasets)
• 新任务(New Tasks)
• 其他(Others)

Backbone

Integrally Pre-Trained Transformer Pyramid Networks

• Paper: https://arxiv.org/abs/2211.12735
• Code: https://github.com/sunsmarterjie/iTPN

Stitchable Neural Networks

• Homepage: https://snnet.github.io/
• Paper: https://arxiv.org/abs/2302.06586
• Code: https://github.com/ziplab/SN-Net

Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks

• Paper: https://arxiv.org/abs/2303.03667
• Code: https://github.com/JierunChen/FasterNet

GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis

• Paper: https://arxiv.org/abs/2301.12959
• Code: https://github.com/tobran/GALIP

MAE

Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders

• Paper: https://arxiv.org/abs/2212.06785
• Code: https://github.com/ZrrSkywalker/I2P-MAE

Generic-to-Specific Distillation of Masked Autoencoders

• Paper: https://arxiv.org/abs/2302.14771
• Code: https://github.com/pengzhiliang/G2SD

NeRF

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior

• Home: https://nope-nerf.active.vision/
• Paper: https://arxiv.org/abs/2212.07388
• Code: None

Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures

• Paper: https://arxiv.org/abs/2211.07600
• Code: https://github.com/eladrich/latent-nerf

NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis

• Paper: https://arxiv.org/abs/2301.08556
• Code: None

Panoptic Lifting for 3D Scene Understanding with Neural Fields

• Homepage: https://nihalsid.github.io/panoptic-lifting/
• Paper: https://arxiv.org/abs/2212.09802
• Code: None

DETR

DETRs with Hybrid Matching

• Paper: https://arxiv.org/abs/2207.13080
• Code: https://github.com/HDETR

NAS

PA&DA: Jointly Sampling PAth and DAta for Consistent NAS

• Paper: https://arxiv.org/abs/2302.14772
• Code: https://github.com/ShunLu91/PA-DA

Diffusion Models(扩散模型)

Video Probabilistic Diffusion Models in Projected Latent Space

• Homepage: https://sihyun.me/PVDM/
• Paper: https://arxiv.org/abs/2302.07685
• Code: https://github.com/sihyun-yu/PVDM

Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models

• Paper: https://arxiv.org/abs/2211.10655
• Code: None

Imagic: Text-Based Real Image Editing with Diffusion Models

• Homepage: https://imagic-editing.github.io/
• Paper: https://arxiv.org/abs/2210.09276
• Code: None

Parallel Diffusion Models of Operator and Image for Blind Inverse Problems

• Paper: https://arxiv.org/abs/2211.10656
• Code: None

DiffRF: Rendering-guided 3D Radiance Field Diffusion

• Homepage: https://sirwyver.github.io/DiffRF/
• Paper: https://arxiv.org/abs/2212.01206
• Code: None

MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

• Paper: https://arxiv.org/abs/2212.09478
• Code: https://github.com/researchmm/MM-Diffusion

HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising

• Homepage: https://aminshabani.github.io/housediffusion/
• Paper: https://arxiv.org/abs/2211.13287
• Code: https://github.com/aminshabani/house_diffusion

Vision Transformer

Integrally Pre-Trained Transformer Pyramid Networks

• Paper: https://arxiv.org/abs/2211.12735
• Code: https://github.com/sunsmarterjie/iTPN

Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors

• Homepage: https://niessnerlab.org/projects/hou2023mask3d.html
• Paper: https://arxiv.org/abs/2302.14746
• Code: None

Learning Trajectory-Aware Transformer for Video Super-Resolution

• Paper: https://arxiv.org/abs/2204.04216
• Code: https://github.com/researchmm/TTVSR

Vision Transformers are Parameter-Efficient Audio-Visual Learners

• Homepage: https://yanbo.ml/project_page/LAVISH/

• Code: https://github.com/GenjiB/LAVISH

Avatars

Structured 3D Features for Reconstructing Relightable and Animatable Avatars

• Homepage: https://enriccorona.github.io/s3f/
• Paper: https://arxiv.org/abs/2212.06820
• Code: None
• Demo: https://www.youtube.com/watch?v=mcZGcQ6L-2s

视觉和语言(Vision-Language)

GIVL: Improving Geographical Inclusivity of Vision-Language Models with Pre-Training Methods

• Paper: https://arxiv.org/abs/2301.01893
• Code: None

Teaching Structured Vision&Language Concepts to Vision&Language Models

• Paper: https://arxiv.org/abs/2211.11733
• Code: None

Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks

• Paper: https://arxiv.org/abs/2211.09808
• Code: https://github.com/fundamentalvision/Uni-Perceiver

Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training

• Paper: https://arxiv.org/abs/2303.00040
• Code: None

CapDet: Unifying Dense Captioning and Open-World Detection Pretraining

• Paper: https://arxiv.org/abs/2303.02489
• Code: None

FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks

• Paper: https://arxiv.org/abs/2303.02483
• Code: None

Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding

• Homepage: https://rllab-snu.github.io/projects/Meta-Explore/doc.html
• Paper: https://arxiv.org/abs/2303.04077
• Code: None

All in One: Exploring Unified Video-Language Pre-training

• Paper: https://arxiv.org/abs/2203.07303
• Code: https://github.com/showlab/all-in-one

Position-guided Text Prompt for Vision Language Pre-training

• Paper: https://arxiv.org/abs/2212.09737
• Code: https://github.com/sail-sg/ptp

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

• Paper: https://arxiv.org/abs/2209.14941
• Code: https://github.com/yanmin-wu/EDA

目标检测(Object Detection)

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

• Paper: https://arxiv.org/abs/2207.02696
• Code: https://github.com/WongKinYiu/yolov7

DETRs with Hybrid Matching

• Paper: https://arxiv.org/abs/2207.13080
• Code: https://github.com/HDETR

Enhanced Training of Query-Based Object Detection via Selective Query Recollection

• Paper: https://arxiv.org/abs/2212.07593
• Code: https://github.com/Fangyi-Chen/SQR

目标跟踪(Object Tracking)

Simple Cues Lead to a Strong Multi-Object Tracker

• Paper: https://arxiv.org/abs/2206.04656
• Code: None

参考图像分割(Referring Image Segmentation )

• Paper: https://arxiv.org/abs/2302.07387

• Code: None

超分辨率(Video Super-Resolution)

视频超分辨率

Learning Trajectory-Aware Transformer for Video Super-Resolution

• Paper: https://arxiv.org/abs/2204.04216

• Code: https://github.com/researchmm/TTVSR

3D语义场景补全(3D Semantic Scene Completion)

• Paper: https://arxiv.org/abs/2302.12251
• Code: https://github.com/NVlabs/VoxFormer

图像生成(Image Generation)

GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis

• Paper: https://arxiv.org/abs/2301.12959
• Code: https://github.com/tobran/GALIP

视频生成(Video Generation)

MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

• Paper: https://arxiv.org/abs/2212.09478
• Code: https://github.com/researchmm/MM-Diffusion

知识蒸馏(Knowledge Distillation)

Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation

• Paper: https://arxiv.org/abs/2302.14290
• Code: None

Generic-to-Specific Distillation of Masked Autoencoders

• Paper: https://arxiv.org/abs/2302.14771
• Code: https://github.com/pengzhiliang/G2SD

三维重建(3D Reconstruction)

OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields

• Paper: https://arxiv.org/abs/2211.12886
• Code: None

SparsePose: Sparse-View Camera Pose Regression and Refinement

• Paper: https://arxiv.org/abs/2211.16991
• Code: None

NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction

• Paper: https://arxiv.org/abs/2303.02375
• Code: None

Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition

• Homepage: https://moygcc.github.io/vid2avatar/
• Paper: https://arxiv.org/abs/2302.11566
• Code: https://github.com/MoyGcc/vid2avatar
• Demo: https://youtu.be/EGi47YeIeGQ

To fit or not to fit: Model-based Face Reconstruction and Occlusion Segmentation from Weak Supervision

• Paper: https://arxiv.org/abs/2106.09614
• Code: https://github.com/unibas-gravis/Occlusion-Robust-MoFA

深度估计(Depth Estimation)

Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation

• Paper: https://arxiv.org/abs/2211.13202
• Code: https://github.com/noahzn/Lite-Mono

轨迹预测(Trajectory Prediction)

IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction

• Paper: https://arxiv.org/abs/2303.00575
• Code: None

视觉问答(Visual Question Answering)

MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering

• Paper: https://arxiv.org/abs/2303.01239
• Code: https://github.com/jingjing12110/MixPHM

手语识别(Sign Language Recognition)

Continuous Sign Language Recognition with Correlation Network

Paper: https://arxiv.org/abs/2303.03202

Code: https://github.com/hulianyuyy/CorrNet

视频预测(Video Prediction)

MOSO: Decomposing MOtion, Scene and Object for Video Prediction

• Paper: https://arxiv.org/abs/2303.03684
• Code: https://github.com/anonymous202203/MOSO

数据集(Datasets)

Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes

• Paper: https://arxiv.org/abs/2303.02760
• Code: None

其他(Others)

Interactive Segmentation as Gaussian Process Classification

• Paper: https://arxiv.org/abs/2302.14578
• Code: None

Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger

• Paper: https://arxiv.org/abs/2302.14677
• Code: None

SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries

• Homepage: http://bit.ly/splinecam
• Paper: https://arxiv.org/abs/2302.12828
• Code: None

SCOTCH and SODA: A Transformer Video Shadow Detection Framework

• Paper: https://arxiv.org/abs/2211.06885
• Code: None

DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization

• Homepage: https://ai4ce.github.io/DeepMapping2/
• Paper: https://arxiv.org/abs/2212.06331
• None: https://github.com/ai4ce/DeepMapping2

RelightableHands: Efficient Neural Relighting of Articulated Hand Models

• Homepage: https://sh8.io/#/relightable_hands
• Paper: https://arxiv.org/abs/2302.04866
• Code: None

Token Turing Machines

• Paper: https://arxiv.org/abs/2211.09119
• Code: None

Single Image Backdoor Inversion via Robust Smoothed Classifiers

• Paper: https://arxiv.org/abs/2303.00215
• Code: https://github.com/locuslab/smoothinv

To fit or not to fit: Model-based Face Reconstruction and Occlusion Segmentation from Weak Supervision

• Paper: https://arxiv.org/abs/2106.09614
• Code: https://github.com/unibas-gravis/Occlusion-Robust-MoFA

HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

• Homepage: https://dolorousrtur.github.io/hood/
• Paper: https://arxiv.org/abs/2212.07242
• Code: https://github.com/dolorousrtur/hood
• Demo: https://www.youtube.com/watch?v=cBttMDPrUYY

A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others

• Paper: https://arxiv.org/abs/2212.04825
• Code: https://github.com/facebookresearch/Whac-A-Mole.git

RelightableHands: Efficient Neural Relighting of Articulated Hand Models

• Homepage: https://sh8.io/#/relightable_hands
• Paper: https://arxiv.org/abs/2302.04866
• Code: None
• Demo: https://sh8.io/static/media/teacher_video.923d87957fe0610730c2.mp4

Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation

• Paper: https://arxiv.org/abs/2303.00914
• Code: None

Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression

• Paper: https://arxiv.org/abs/2303.01052
• Code: None

UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy

• Paper: https://arxiv.org/abs/2303.00938
• Code: None

Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation with Cross-Scale Distortion Awareness

• Paper: https://arxiv.org/abs/2303.00971
• Code: https://github.com/zhijieshen-bjtu/DOPNet

Learning Neural Parametric Head Models

• Homepage: https://simongiebenhain.github.io/NPHM)
• Paper: https://arxiv.org/abs/2212.02761
• Code: None

A Meta-Learning Approach to Predicting Performance and Data Requirements

• Paper: https://arxiv.org/abs/2303.01598
• Code: None

MACARONS: Mapping And Coverage Anticipation with RGB Online Self-Supervision

• Homepage: https://imagine.enpc.fr/~guedona/MACARONS/
• Paper: https://arxiv.org/abs/2303.03315
• Code: None

Masked Images Are Counterfactual Samples for Robust Fine-tuning

• Paper: https://arxiv.org/abs/2303.03052
• Code: None

HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling

• Paper: https://arxiv.org/abs/2303.02700
• Code: None

Decompose, Adjust, Compose: Effective Normalization by Playing with Frequency for Domain Generalization

• Paper: https://arxiv.org/abs/2303.02328
• Code: None

Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization

• Paper: https://arxiv.org/abs/2303.03108
• Code: None

Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples

• Paper: https://arxiv.org/abs/2301.01217
• Code: https://github.com/jiamingzhang94/Unlearnable-Clusters