CVPR'23 最新 99 篇论文分方向整理｜涵盖神经网络结构、医学影像、图像

芷亦止兮

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919篇，项目地址：https://www.cvmart.net/community/detail/7422
以下是最近更新的 CVPR 2023 论文，涵盖神经网络结构、医学影像、ReId、图像去雾、异常检测等方向。
下载地址：https://www.cvmart.net/community/detail/7520
2D目标检测(2D Object Detection)

[1]Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision
paper：https://arxiv.org/abs/2304.01484
code：https://github.com/xinyiying/lesps

[2]Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen Domains
paper：https://arxiv.org/abs/2304.02950


[3]Continual Detection Transformer for Incremental Object Detection
paper：https://arxiv.org/abs/2304.03110


[4]DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment
paper：https://arxiv.org/abs/2304.04514


[5]Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection
paper：https://arxiv.org/abs/2304.05098


3D目标检测(3D object detection)

[1]Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection
paper：https://arxiv.org/abs/2304.01464
code：https://github.com/azhuantou/hssda


[2]Curricular Object Manipulation in LiDAR-based Object Detection
paper：https://arxiv.org/abs/2304.04248
code：https://github.com/zzy816/com


人物交互检测(HOI Detection)

[1]Instant-NVR: Instant Neural Volumetric Rendering for Human-object Interactions from Monocular RGBD Stream
paper：https://arxiv.org/abs/2304.03184


[2]Relational Context Learning for Human-Object Interaction Detection
paper：https://arxiv.org/abs/2304.04997


异常检测(Anomaly Detection)

[1]Robust Outlier Rejection for 3D Registration with Variational Bayes
paper：https://arxiv.org/abs/2304.01514
code：https://github.com/jiang-hb/vbreg

[2]Video Event Restoration Based on Keyframes for Video Anomaly Detection
paper：https://arxiv.org/abs/2304.05112


语义分割(Semantic Segmentation)

[1]DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation
paper：https://arxiv.org/abs/2304.02222
code：https://github.com/fy-vision/diga


[2]Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation
paper：https://arxiv.org/abs/2304.02991
code：https://github.com/cvlab-unibo/mm2d3d

[3]Federated Incremental Semantic Segmentation
paper：https://arxiv.org/abs/2304.04620
code：https://github.com/jiahuadong/fiss


[4]Continual Semantic Segmentation with Automatic Memory Sample Selection
paper：https://arxiv.org/abs/2304.05015


深度估计(Depth Estimation)

[1]EGA-Depth: Efficient Guided Attention for Self-Supervised Multi-Camera Depth Estimation
paper：https://arxiv.org/abs/2304.03369


[2]DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium
paper：https://arxiv.org/abs/2304.03560
code：https://github.com/antabangun/dualrefine


人体解析/人体姿态估计(Human Parsing/Human Pose Estimation)

[1]A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image
paper：https://arxiv.org/abs/2304.03635
code：https://github.com/changlongjianggit/a2j-transformer


[2]Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field Registration
paper：https://arxiv.org/abs/2304.04437
code：https://github.com/tobibaum/partialsportsfieldreg_3dhpe

[3]DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback
paper：https://arxiv.org/abs/2304.04496


视频处理(Video Processing)

[1]BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation
paper：https://arxiv.org/abs/2304.02225
code：https://github.com/junheum/biformer


超分辨率(Super Resolution)

[1]Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
paper：https://arxiv.org/abs/2304.03542


图像复原/图像增强/图像重建(Image Restoration/Image Reconstruction)

[1]Generative Diffusion Prior for Unified Image Restoration and Enhancement
paper：https://arxiv.org/abs/2304.01247


[2]CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees
paper：https://arxiv.org/abs/2304.04708


图像去噪/去模糊/去雨去雾(Image Denoising)

[1]HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering
paper：https://arxiv.org/abs/2304.01686


[2]RIDCP: Revitalizing Real Image Dehazing via High-Quality
codebook Priors
paper：https://arxiv.org/abs/2304.03994
code：https://github.com/RQ-Wu/RIDCP_dehazing

人脸识别/检测(Facial Recognition/Detection)

[1]Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention
paper：https://arxiv.org/abs/2304.02284


[2]Micron-BERT: BERT-based Facial Micro-Expression Recognition
paper：https://arxiv.org/abs/2304.03195
code：https://github.com/uark-cviu/micron-bert


人脸生成/合成/重建/编辑(Face Generation/Face Synthesis/Face Reconstruction/Face Editing)

[1]Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos
paper：https://arxiv.org/abs/2304.01436


[2]StyleGAN Salon: Multi-View Latent Optimization for Pose-Invariant Hairstyle Transfer
paper：https://arxiv.org/abs/2304.02744


[3]GANHead: Towards Generative Animatable Neural Head Avatars
paper：https://arxiv.org/abs/2304.03950


目标跟踪(Object Tracking)

[1]Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
paper：https://arxiv.org/abs/2304.01893


[2]Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction
paper：https://arxiv.org/abs/2304.04298
code：https://github.com/viewsetting/unsupervised_sampling_promoting


图像&视频检索/视频理解(Image&Video Retrieval/Video Understanding)

[1]Improving Image Recognition by Retrieving from Web-Scale Image-Text Data
paper：https://arxiv.org/abs/2304.05173


行人重识别/检测(Re-Identification/Detection)

[1]PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification
paper：https://arxiv.org/abs/2304.01537


[2]Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification
paper：https://arxiv.org/abs/2304.04205
code：https://github.com/jiawei151/sgiel_vireid

图像/视频字幕(Image/Video Caption)

[1]Cross-Domain Image Captioning with Discriminative Finetuning
paper：https://arxiv.org/abs/2304.01662
code：https://github.com/facebookresearch/EGG


[2]Model-Agnostic Gender Debiased Image Captioning
paper：https://arxiv.org/abs/2304.03693


医学影像(Medical Imaging)

[1]Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
paper：https://arxiv.org/abs/2304.02255


[2]Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations
paper：https://arxiv.org/abs/2304.04077
code：https://github.com/danielf29/prototipical_parts


[3]Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis
paper：https://arxiv.org/abs/2304.04579
code：https://github.com/cristianopatricio/coherent-cbe-skin

图像生成/图像合成(Image Generation/Image Synthesis)

[1]Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation
paper：https://arxiv.org/abs/2304.01816


[2]Few-shot Semantic Image Synthesis with Class Affinity Transfer
paper：https://arxiv.org/abs/2304.02321


点云(Point Cloud)

[1]MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds
paper：https://arxiv.org/abs/2304.01554
code：https://github.com/sinashish/mensa_mtda


场景重建/视图合成/新视角合成(Novel View Synthesis)

[1]Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field
paper：https://arxiv.org/abs/2304.03526


[2]POEM: Reconstructing Hand in a Point Embedded Multi-view Stereo
paper：https://arxiv.org/abs/2304.04038
code：https://github.com/lixiny/poem


[3]Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos
paper：https://arxiv.org/abs/2304.04452


[4]Neural Lens Modeling
paper：https://arxiv.org/abs/2304.04848


[5]One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field
paper：https://arxiv.org/abs/2304.05097


[6]MonoHuman: Animatable Human Neural Field from Monocular Video
paper：https://arxiv.org/abs/2304.02001


[7]GINA-3D: Learning to Generate Implicit Neural Assets in the Wild
paper：https://arxiv.org/abs/2304.02163


[8]Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes
paper：https://arxiv.org/abs/2304.03266


文本检测/识别/理解(Text Detection/Recognition/Understanding)

[1]Towards Unified Scene Text Spotting based on Sequence Generation
paper：https://arxiv.org/abs/2304.03435


神经网络结构设计(Neural Network Structure Design)

[1]SMPConv: Self-moving Point Representations for Continuous Convolution
paper：https://arxiv.org/abs/2304.02330
code：https://github.com/sangnekim/smpconv


CNN

[1]VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
paper：https://arxiv.org/abs/2304.01434
code：https://github.com/jaeill/CVPR23-VNE


Transformer

[1]METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens
paper：https://arxiv.org/abs/2304.02211


[2]MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection
paper：https://arxiv.org/abs/2304.02767


[3]Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention
paper：https://arxiv.org/abs/2304.03282
code：https://github.com/dingmyu/dependencyvit


[4]Slide-Transformer: Hierarchical Vision Transformer with Local Self-Attention
paper：https://arxiv.org/abs/2304.04237
code：https://github.com/leaplabthu/slide-transformer


图神经网络(GNN)

[1]Adversarially Robust Neural Architecture Search for Graph Neural Networks
paper：https://arxiv.org/abs/2304.04168


归一化/正则化(Batch Normalization)

[1]Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling
paper：https://arxiv.org/abs/2304.03937


模型训练/泛化(Model Training/Generalization)

[1]Re-thinking Model Inversion Attacks Against Deep Neural Networks
paper：https://arxiv.org/abs/2304.01669


[2]Improved Test-Time Adaptation for Domain Generalization
paper：https://arxiv.org/abs/2304.04494


长尾分布(Long-Tailed Distribution)

[1]Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation
paper：https://arxiv.org/abs/2304.01279
code：https://github.com/jinyan-06/shike


视觉表征学习(Visual Representation Learning)

[1]HNeRV: A Hybrid Neural Representation for Videos
paper：https://arxiv.org/abs/2304.02633
code：https://github.com/haochen-rye/hnerv


多模态学习(Multi-Modal Learning)

[1]Detecting and Grounding Multi-Modal Media Manipulation
paper：https://arxiv.org/abs/2304.02556
code：https://github.com/rshaojimmy/multimodal-deepfake


[2]Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-commerce
paper：https://arxiv.org/abs/2304.02853


[3]Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
paper：https://arxiv.org/abs/2304.03307
code：https://github.com/talalwasim/vita-clip


视觉-语言（Vision-language）

[1]Learning to Name Classes for Vision and Language Models
paper：https://arxiv.org/abs/2304.01830


[2]VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision
paper：https://arxiv.org/abs/2304.03135
code：https://github.com/lmy98129/vlpd


[3]CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model
paper：https://arxiv.org/abs/2304.04231
code：https://github.com/dk-liang/crowdclip


[4]Improving Vision-and-Language Navigation by Generating Future-View Image Semantics
paper：https://arxiv.org/abs/2304.04907


场景图生成(Scene Graph Generation)

[1]Devil's on the Edges: Selective Quad Attention for Scene Graph Generation
paper：https://arxiv.org/abs/2304.03495


视觉推理/视觉问答(Visual Reasoning/VQA)

[1]Language Models are Causal Knowledge Extractors for Zero-shot Video Question Answering
paper：https://arxiv.org/abs/2304.03754


数据集(Dataset)

[1]Uncurated Image-Text Datasets: Shedding Light on Demographic Bias
paper：https://arxiv.org/abs/2304.02828
code：https://github.com/noagarcia/phase
小样本学习/零样本学习(Few-shot Learning/Zero-shot Learning)

[1]Zero-shot Generative Model Adaptation via Image-specific Prompt Learning
paper：https://arxiv.org/abs/2304.03119


迁移学习/domain/自适应(Transfer Learning/Domain Adaptation)

[1]DATE: Domain Adaptive Product Seeker for E-commerce
paper：https://arxiv.org/abs/2304.03669


[2]Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer
paper：https://arxiv.org/abs/2304.04461


持续学习(Continual Learning/Life-long Learning)

[1]Asynchronous Federated Continual Learning
paper：https://arxiv.org/abs/2304.03626
code：https://github.com/lttm/fedspace


[2]Exploring Data Geometry for Continual Learning
paper：https://arxiv.org/abs/2304.03931


[3]Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning
paper：https://arxiv.org/abs/2304.05288
code：https://github.com/wenjinw/par


[4]Online Distillation with Continual Learning for Cyclic Domain Shifts
paper：https://arxiv.org/abs/2304.01239


视觉定位/位姿估计(Visual Localization/Pose Estimation)

[1]OrienterNet: Visual Localization in 2D Public Maps with Neural Matching
paper：https://arxiv.org/abs/2304.02009


增量学习(Incremental Learning)

[1]On the Stability-Plasticity Dilemma of Class-Incremental Learning
paper：https://arxiv.org/abs/2304.01663


[2]PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning
paper：https://arxiv.org/abs/2304.04408


强化学习(Reinforcement Learning)

[1]Reinforcement Learning-Based Black-Box Model Inversion Attacks
paper：https://arxiv.org/abs/2304.04625


元学习(Meta Learning)

[1]Meta-causal Learning for Single Domain Generalization
paper：https://arxiv.org/abs/2304.03709


[2]Meta Compositional Referring Expression Segmentation
paper：https://arxiv.org/abs/2304.04415


[3]Meta-Learning with a Geometry-Adaptive Preconditioner
paper：https://arxiv.org/abs/2304.01552
code：https://github.com/suhyun777/cvpr23-gap


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]Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model
paper：https://arxiv.org/abs/2304.03572


[2]Token Boosting for Robust Self-Supervised Visual Transformer Pre-training
paper：https://arxiv.org/abs/2304.04175


[3]SOOD: Towards Semi-Supervised Oriented Object Detection
paper：https://arxiv.org/abs/2304.04515
code：https://github.com/hamperdredes/sood

[4]Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning
paper：https://arxiv.org/abs/2304.01482
code：https://github.com/ucdvision/patchsearch


神经网络可解释性(Neural Network Interpretability)

[1]Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning
paper：https://arxiv.org/abs/2304.04824


图像计数(Image Counting)

[1]Density Map Distillation for Incremental Object Counting
paper：https://arxiv.org/abs/2304.05255


其他

[1]Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification
paper：https://arxiv.org/abs/2304.01804
code：https://github.com/youngwk/bridgegapexplanationpamc


[2]Knowledge Combination to Learn Rotated Detection Without Rotated Annotation
paper：https://arxiv.org/abs/2304.02199


[3]CloSET: Modeling Clothed Humans on Continuous Surface with Explicit Template Decomposition
paper：https://arxiv.org/abs/2304.03167


[4]DC2: Dual-Camera Defocus Control by Learning to Refocus
paper：https://arxiv.org/abs/2304.0328

来源：https://www.bilibili.com/read/cv23266416
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