CVPR'23 最新 70 篇论文分方向整理｜包含目标检测、图像处理、人脸、医学影像、半监督学习等方向 |
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CVPR'23 最新 70 篇论文分方向整理｜包含目标检测、图像处理、人脸、医学影像、半监督学习等方向
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编辑丨极市平台 CVPR2023已经放榜，今年有2360篇，接收率为25.78%。在CVPR2023正式会议召开前，为了让大家更快地获取和学习到计算机视觉前沿技术，极市对CVPR023 最新论文进行追踪，包括分研究方向的论文、代码汇总以及论文技术直播分享。 CVPR 2023 论文分方向整理目前在极市社区持续更新中，已累计更新了158篇，项目地址：https://www.cvmart.net/community/detail/7422 以下是最近更新的 CVPR 2023 论文，包含目标检测、图像处理、人脸、场景重建、医学影像、半监督学习/弱监督学习/无监督学习/自监督学习等方向。 可打包下载：https://www.cvmart.net/community/detail/7429 检测2D目标检测(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  视频目标检测(Video Object Detection[1]SCOTCH and SODA: A Transformer Video Shadow Detection Frameworkpaper：https://arxiv.org/abs/2211.06885 3D目标检测(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 目标检测的Query Contrast Voxel-DETR paper：https://arxiv.org/abs/2212.07289 code：https://github.com/poodarchu/ConQueR 显著性目标检测(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 车道线检测(Lane Detection[1]BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline paper：https://arxiv.org/abs/2210.06006  异常检测(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图像分割(Image Segmentation[1]MP-Former: Mask-Piloted Transformer for Image Segmentationpaper：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 语义分割(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 实例分割(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：将图像分割表述为顺序多边形生成 paper：https://arxiv.org/abs/2302.07387 目标跟踪(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 图像处理(Image Processing超分辨率(Super Resolution[1]Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild(野外鲁棒图像超分辨率的去噪扩散概率模型 paper：https://arxiv.org/abs/2302.07864 project：https://sihyun.me/PVDM/ 图像复原/图像增强/图像重建(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 图像去噪/去模糊/去雨去雾(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 图像编辑/图像修复(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 图像质量评估(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 图像配准(Image Registration[1]Indescribable Multi-modal Spatial Evaluator paper：https://arxiv.org/abs/2303.00369 code：https://github.com/Kid-Liet/IMSE/pulse 人脸(Face人脸生成/合成/重建/编辑(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：具有快速个性化适应的身份保持谈话头像生成 paper：https://arxiv.org/abs/2212.08062 code：https://github.com/Meta-Portrait/MetaPortrait 人脸伪造/反欺骗(Face Forgery/Face Anti-Spoofing[1]Physical-World Optical Adversarial Attacks on 3D Face Recognition paper：https://arxiv.org/abs/2205.13412 医学影像(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 图像生成/图像合成(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(使用预训练的 2D 扩散模型解决 3D 逆问题 paper：https://arxiv.org/abs/2211.10655 [5]Parallel Diffusion Models of Operator and Image for Blind Inverse Problems(盲反问题算子和图像的并行扩散模型 paper：https://arxiv.org/abs/2211.10656 场景重建/视图合成/新视角合成(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/ 多模态学习(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(通过最大化多模态互信息实现一体化预训练 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：用于大规模视觉和视觉语言任务的通才模型 paper：https://arxiv.org/abs/2211.09808 code：https://github.com/fundamentalvision/Uni-Perceiver  半监督学习/弱监督学习/无监督学习/自监督学习(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/~xdwang/projects/CutLER/ |
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