【论文合集】CVPR2023年 部分论文

参考：

CVPR 2023 最全整理：论文分方向汇总 / 代码 / 解读 / 直播 / 项目（更新中）【计算机视觉】-极市开发者社区 (cvmart.net)

amusi/CVPR2023-Papers-with-Code: CVPR 2023 论文和开源项目合集 (github.com)

GAN/生成式/对抗式(GAN/Generative/Adversarial)

[7]Fine-Grained Face Swapping via Regional GAN Inversion
paper

[6]Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
paper

[5]Graph Transformer GANs for Graph-Constrained House Generation
paper

[4]Improving GAN Training via Feature Space Shrinkage
paper | code

[3]Adversarial Attack with Raindrops
paper

[2]T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
paper | project

[1]Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars
paper | project

模型训练/泛化(Model Training/Generalization)

[25]Improved Test-Time Adaptation for Domain Generalization
paper

[24]Re-thinking Model Inversion Attacks Against Deep Neural Networks
paper

[23]Regularize implicit neural representation by itself
paper

[22]Improving the Transferability of Adversarial Samples by Path-Augmented Method
paper

[21]Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency
paper | code

[20]Progressive Random Convolutions for Single Domain Generalization
paper

[19]Tunable Convolutions with Parametric Multi-Loss Optimization
paper

[18]Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
paper | code

[17]CFA: Class-wise Calibrated Fair Adversarial Training
paper | code

[16]Generalist: Decoupling Natural and Robust Generalization
paper

[15]Feature Separation and Recalibration for Adversarial Robustness
paper

[14]Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck
paper

[13]FlexiViT: One Model for All Patch Sizes
paper | code

[12]Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization
paper | code

[11]Improving Generalization with Domain Convex Game
paper

[10]TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization
paper | code

[9]An Extended Study of Human-like Behavior under Adversarial Training
paper

[8]Sharpness-Aware Gradient Matching for Domain Generalization
paper | code

[7]HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining
paper

[6]Universal Instance Perception as Object Discovery and Retrieval
paper | code

[5]Practical Network Acceleration with Tiny Sets
paper | code

[4]Towards Bridging the Performance Gaps of Joint Energy-based Models
paper | code

[3]DropKey
paper

[2]Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization
paper

[1]DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks
paper

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

[17]DATE: Domain Adaptive Product Seeker for E-commerce
paper

[16]Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer
paper

[15]GeoNet: Benchmarking Unsupervised Adaptation across Geographies
paper

[14]C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
paper

[13]AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation
paper | code

[12]BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
paper | code

[11]Deep Frequency Filtering for Domain Generalization
paper

[10]Semi-Supervised Domain Adaptation with Source Label Adaptation
paper | code

[9]Unsupervised Continual Semantic Adaptation through Neural Rendering
paper

[8]MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
paper | code

[7]Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective
paper

[6]Manipulating Transfer Learning for Property Inference
paper | code

[5]Trainable Projected Gradient Method for Robust Fine-tuning
paper

[4]DA-DETR: Domain Adaptive Detection Transformer with Information Fusion
paper

[3]Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
paper | code

[2]Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation
paper | code

[1]Adaptive Assignment for Geometry Aware Local Feature Matching
paper

对比学习(Contrastive Learning)

[11]FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
paper

[10]Dynamic Conceptional Contrastive Learning for Generalized Category Discovery
paper | code

[9]Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens
paper

[8]PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery
paper | code

[7]Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data
paper

[6]Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss
paper

[5]Positive-Augmented Constrastive Learning for Image and Video Captioning Evaluation
paper | code

[4]MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset
paper | code

[3]CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning
paper | code

[2]Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation
paper | code

[1]Twin Contrastive Learning with Noisy Labels
paper | code

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)

[29]Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model
paper

[28]Token Boosting for Robust Self-Supervised Visual Transformer Pre-training
paper

[27]SOOD: Towards Semi-Supervised Oriented Object Detection
paper | code

[26]Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning
paper | code

[25]Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
paper | code

[24]Siamese DETR
paper

[23]HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
paper

[22]Detecting Backdoors in Pre-trained Encoders
paper | code

[21]Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders
paper

[20]Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation
paper | code

[19]ProtoCon: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised Learning
paper

[18]Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
paper

[17]Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching
paper | code

[16]Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
paper

[15]Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning
paper

[14]Correlational Image Modeling for Self-Supervised Visual Pre-Training
paper

[13]Extracting Class Activation Maps from Non-Discriminative Features as well
paper | code

[12]TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation
paper | code

[11]LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding
paper

[10]MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
paper | code

[9]Semi-supervised Hand Appearance Recovery via Structure Disentanglement and Dual Adversarial Discrimination
paper

[8]Non-Contrastive Unsupervised Learning of Physiological Signals from Video
paper

[7]Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems
paper | code

[6]Intrinsic Physical Concepts Discovery with Object-Centric Predictive Models
paper

[5]The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
paper | code

[4]Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning
paper | code

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

[2]Siamese Image Modeling for Self-Supervised Vision Representation Learning
paper | code

[1]Cut and Learn for Unsupervised Object Detection and Instance Segmentation
paper | project文章来源地址https://www.toymoban.com/news/detail-652137.html

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