編輯丨極市平臺(tái)

CVPR2023已經(jīng)放榜，今年有2360篇，接收率為25.78%。在CVPR2023正式會(huì)議召開(kāi)前，為了讓大家更快地獲取和學(xué)習(xí)到計(jì)算機(jī)視覺(jué)前沿技術(shù)，極市對(duì)CVPR023 最新論文進(jìn)行追蹤，包括分研究方向的論文、代碼匯總以及論文技術(shù)直播分享。

CVPR 2023 論文分方向整理目前在極市社區(qū)持續(xù)更新中，已累計(jì)更新了158篇，項(xiàng)目地址：https://www.cvmart.net/community/detail/7422

以下是最近更新的 CVPR 2023 論文，包含目標(biāo)檢測(cè)、圖像處理、人臉、場(chǎng)景重建、醫(yī)學(xué)影像、半監(jiān)督學(xué)習(xí)/弱監(jiān)督學(xué)習(xí)/無(wú)監(jiān)督學(xué)習(xí)/自監(jiān)督學(xué)習(xí)等方向。

可打包下載：https://www.cvmart.net/community/detail/7429

檢測(cè)

2D目標(biāo)檢測(cè)(2D Object Detection

[1]CapDet: Unifying Dense Captioning and Open-World Detection Pretraining

paper：https://arxiv.org/abs/2303.02489

[2]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

[3]DETRs with Hybrid Matching

paper：https://arxiv.org/abs/2207.13080

code：https://github.com/HDETR

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

paper：https://arxiv.org/abs/2207.02696

code：https://github.com/WongKinYiu/yolov7

視頻目標(biāo)檢測(cè)(Video Object Detection

[1]SCOTCH and SODA: A Transformer Video Shadow Detection Framework
paper：https://arxiv.org/abs/2211.06885

3D目標(biāo)檢測(cè)(3D object detection

[1]MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection

paper：https://arxiv.org/abs/2209.03102

code：https://github.com/sxjyjay/msmdfusion

[2]Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection

paper：https://arxiv.org/abs/2303.06880

code：https://github.com/PJLab-ADG/3DTrans

[3]LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion

paper：https://arxiv.org/abs/2303.03595

code：https://github.com/sankin97/LoGoNet

[4]ConQueR: Query Contrast Voxel-DETR for 3D Object Detection(3D 目標(biāo)檢測(cè)的Query Contrast Voxel-DETR

paper：https://arxiv.org/abs/2212.07289

code：https://github.com/poodarchu/ConQueR

顯著性目標(biāo)檢測(cè)(Saliency Object Detection

[1]Texture-guided Saliency Distilling for Unsupervised Salient Object Detection

paper：https://arxiv.org/abs/2207.05921

code：https://github.com/moothes/A2S-v2

車道線檢測(cè)(Lane Detection

[1]BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline

paper：https://arxiv.org/abs/2210.06006

異常檢測(cè)(Anomaly Detection

[1]Block Selection Method for Using Feature Norm in Out-of-distribution Detection

paper：https://arxiv.org/abs/2212.02295

[2]Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection

paper：https://arxiv.org/abs/2212.02303

[3]Multimodal Industrial Anomaly Detection via Hybrid Fusion

paper：https://arxiv.org/abs/2303.00601

code：https://github.com/nomewang/M3DM

分割(Segmentation

圖像分割(Image Segmentation

[1]MP-Former: Mask-Piloted Transformer for Image Segmentation
paper：https://arxiv.org/abs/2303.07336

code：https://github.com/IDEA-Research/MP-Former

[2]Interactive Segmentation as Gaussian Process Classification

paper：https://arxiv.org/abs/2302.14578

語(yǔ)義分割(Semantic Segmentation

[1]Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

paper：http://arxiv.org/abs/2210.04150

code：https://github.com/facebookresearch/ov-seg

[2]Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos

paper：https://arxiv.org/abs/2303.07224

code：https://github.com/THU-LYJ-Lab/AR-Seg

[3]SCPNet: Semantic Scene Completion on Point Cloud

paper：https://arxiv.org/abs/2303.06884

[4]On Calibrating Semantic Segmentation Models: Analyses and An Algorithm

paper：https://arxiv.org/abs/2212.12053

[5]Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision

paper：https://arxiv.org/abs/2301.09121

[6]Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation

paper：https://arxiv.org/abs/2208.09910

code：https://github.com/LiheYoung/UniMatch

[7]Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation

paper：https://arxiv.org/abs/2302.14250

實(shí)例分割(Instance Segmentation

[1]ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution

paper：https://arxiv.org/abs/2303.00246

[22]PolyFormer: Referring Image Segmentation as Sequential Polygon Generation(PolyFormer：將圖像分割表述為順序多邊形生成

paper：https://arxiv.org/abs/2302.07387

目標(biāo)跟蹤(Object Tracking

[1]Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking

paper：https://arxiv.org/abs/2203.14360v2

code：https://github.com/noahcao/OC_SORT

[2]Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation

paper：https://arxiv.org/abs/2302.14589

[3]Referring Multi-Object Tracking

paper：https://arxiv.org/abs/2303.03366

[4]Simple Cues Lead to a Strong Multi-Object Tracker

paper：https://arxiv.org/abs/2206.04656

圖像處理(Image Processing

超分辨率(Super Resolution

[1]Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild(野外魯棒圖像超分辨率的去噪擴(kuò)散概率模型

paper：https://arxiv.org/abs/2302.07864

project：https://sihyun.me/PVDM/

圖像復(fù)原/圖像增強(qiáng)/圖像重建(Image Restoration/Image Reconstruction

[1]Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective

paper：https://arxiv.org/abs/2303.06859

code：https://github.com/lixinustc/Casual-IRDIL

[2]DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration

paper：https://arxiv.org/abs/2303.06885

[3]Robust Unsupervised StyleGAN Image Restoration

paper：https://arxiv.org/abs/2302.06733

[4]Raw Image Reconstruction with Learned Compact Metadata

paper：https://arxiv.org/abs/2302.12995

[5]Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

paper：https://arxiv.org/abs/2303.00748

code：https://github.com/ofsoundof/GRL-Image-Restoration

[6]Imagic: Text-Based Real Image Editing with Diffusion Models

paper：https://arxiv.org/abs/2210.09276

project：https://imagic-editing.github.io/

[7]High-resolution image reconstruction with latent diffusion models from human brain activity

paper：https://www.biorxiv.org/content/10.1101/2022.11.18.517004v2

project：https://sites.google.com/view/stablediffusion-with-brain/

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

paper：https://arxiv.org/abs/2211.10655

圖像去噪/去模糊/去雨去霧(Image Denoising

[1]Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior

paper：https://arxiv.org/abs/2210.05361

[2]Polarized Color Image Denoising using Pocoformer

paper：https://arxiv.org/abs/2207.00215

[3]Blur Interpolation Transformer for Real-World Motion from Blur

paper：https://arxiv.org/abs/2211.11423

code：https://github.com/zzh-tech/BiT

[4]Structured Kernel Estimation for Photon-Limited Deconvolution

paper：https://arxiv.org/abs/2303.03472

code：https://github.com/sanghviyashiitb/structured-kernel-cvpr23

圖像編輯/圖像修復(fù)(Image Edit/Inpainting

[1]LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data

paper：https://arxiv.org/abs/2208.14889

code：https://github.com/KU-CVLAB/LANIT

圖像質(zhì)量評(píng)估(Image Quality Assessment

[1]CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

paper：https://arxiv.org/abs/2112.06592

[2]Quality-aware Pre-trained Models for Blind Image Quality Assessment

paper：https://arxiv.org/abs/2303.00521

圖像配準(zhǔn)(Image Registration

[1]Indescribable Multi-modal Spatial Evaluator

paper：https://arxiv.org/abs/2303.00369

code：https://github.com/Kid-Liet/IMSE/pulse

人臉(Face

人臉生成/合成/重建/編輯(Face Generation/Face Synthesis/Face Reconstruction/Face Editing

[1]A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images

paper：https://arxiv.org/abs/2302.14434

[2]MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation(MetaPortrait：具有快速個(gè)性化適應(yīng)的身份保持談話頭像生成

paper：https://arxiv.org/abs/2212.08062

code：https://github.com/Meta-Portrait/MetaPortrait

人臉偽造/反欺騙(Face Forgery/Face Anti-Spoofing

[1]Physical-World Optical Adversarial Attacks on 3D Face Recognition

paper：https://arxiv.org/abs/2205.13412

醫(yī)學(xué)影像(Medical Imaging

[1]Deep Feature In-painting for Unsupervised Anomaly Detection in X-ray Images

paper：https://arxiv.org/pdf/2111.13495.pdf

code：https://github.com/tiangexiang/SQUID

[2]Label-Free Liver Tumor Segmentation

paper：https://arxiv.org/pdf/2210.14845.pdf

code：https://github.com/MrGiovanni/SyntheticTumors

圖像生成/圖像合成(Image Generation/Image Synthesis

[1]DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

paper：https://arxiv.org/abs/2208.12242

code：https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers/examples/dreambooth

[2]Progressive Open Space Expansion for Open-Set Model Attribution

paper：https://arxiv.org/abs/2303.06877

code：https://github.com/tianyunyoung/pose

[3]Person Image Synthesis via Denoising Diffusion Model

paper：https://arxiv.org/abs/2211.12500

[4]Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models(使用預(yù)訓(xùn)練的 2D 擴(kuò)散模型解決 3D 逆問(wèn)題

paper：https://arxiv.org/abs/2211.10655

[5]Parallel Diffusion Models of Operator and Image for Blind Inverse Problems(盲反問(wèn)題算子和圖像的并行擴(kuò)散模型

paper：https://arxiv.org/abs/2211.10656

場(chǎng)景重建/視圖合成/新視角合成(Novel View Synthesis

[1]3D Video Loops from Asynchronous Input

paper：https://arxiv.org/abs/2303.05312

code：https://github.com/limacv/VideoLoop3D

[2]NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer

paper：https://arxiv.org/abs/2303.06919

code：https://t.co/uNiTd9ujCv

[3]NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation

paper：https://arxiv.org/abs/2212.14710

[4]Renderable Neural Radiance Map for Visual Navigation

paper：https://arxiv.org/abs/2303.00304

[5]Real-Time Neural Light Field on Mobile Devices

paper：https://arxiv.org/abs/2212.08057

project：https://snap-research.github.io/MobileR2L/

[6]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

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

paper：https://arxiv.org/abs/2212.07388

project：https://nope-nerf.active.vision/

多模態(tài)學(xué)習(xí)(Multi-Modal Learning

[1]Align and Attend: Multimodal Summarization with Dual Contrastive Losses

paper：https://arxiv.org/abs/2303.07284

code：https://boheumd.github.io/A2Summ/

[2]Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information(通過(guò)最大化多模態(tài)互信息實(shí)現(xiàn)一體化預(yù)訓(xùn)練

paper：https://arxiv.org/abs/2211.09807

code：https://github.com/OpenGVLab/M3I-Pretraining

[3]Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks(Uni-Perceiver v2：用于大規(guī)模視覺(jué)和視覺(jué)語(yǔ)言任務(wù)的通才模型

paper：https://arxiv.org/abs/2211.09808

code：https://github.com/fundamentalvision/Uni-Perceiver

半監(jiān)督學(xué)習(xí)/弱監(jiān)督學(xué)習(xí)/無(wú)監(jiān)督學(xué)習(xí)/自監(jiān)督學(xué)習(xí)(Self-supervised Learning/Semi-supervised Learning)

[1]The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training

paper：https://arxiv.org/abs/2205.12502

code：https://github.com/gicheonkang/gst-visdial

[2]Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning

paper：https://arxiv.org/abs/2303.06870

code：https://github.com/megvii-research/US3L-CVPR2023

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

paper：https://arxiv.org/abs/2302.14746

[4]Siamese Image Modeling for Self-Supervised Vision Representation Learning

paper：https://arxiv.org/abs/2206.01204

code：https://github.com/fundamentalvision/Siamese-Image-Modeling

[5]Cut and Learn for Unsupervised Object Detection and Instance Segmentation

paper：https://arxiv.org/abs/2301.11320

project：http://people.eecs.berkeley.edu