这里写目录标题

• CVPR2022
• • Label 相关
  • • A Dual Weighting Label Assignment Scheme for Object Detection
    • ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
    • ADeLA_ Automatic Dense Labeling With Attention for Viewpoint Shift in Semantic Segmentation
    • Back to Reality_ Weakly-Supervised 3D Object Detection With Shape-Guided Label Enhancement
    • BoostMIS_ Boosting Medical Image Semi-Supervised Learning With Adaptive Pseudo Labeling and Informative Active Annotation
    • Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition
    • DASO_ Distribution-Aware Semantics-Oriented Pseudo-Label for Imbalanced Semi-Supervised Learning
    • Debiased Learning From Naturally Imbalanced Pseudo-Labels
    • Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement
    • Dist-PU_ Positive-Unlabeled Learning From a Label Distribution Perspective
    • Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation
    • FedCorr_ Multi-Stage Federated Learning for Label Noise Correction
    • Few-Shot Incremental Learning for Label-to-Image Translation
    • Few-Shot Learning With Noisy Labels
    • Improving Segmentation of the Inferior Alveolar Nerve Through Deep Label Propagation
    • Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment
    • Incremental Learning in Semantic Segmentation From Image Labels
    • Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation
    • Interactive Multi-Class Tiny-Object Detection
    • Label Matching Semi-Supervised Object Detection
    • Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification
    • Label, Verify, Correct_ A Simple Few Shot Object Detection Method
    • Label-Only Model Inversion Attacks via Boundary Repulsion
    • Large Loss Matters in Weakly Supervised Multi-Label Classification
    • Large-Scale Pre-training for Person Re-identification with Noisy Labels
    • Learning Fair Classifiers With Partially Annotated Group Labels
    • Learning From Pixel-Level Noisy Label_ A New Perspective for Light Field Saliency Detection
    • Learning To Detect Mobile Objects From LiDAR Scans Without Labels
    • Learning To Imagine_ Diversify Memory for Incremental Learning Using Unlabeled Data
    • Learning With Neighbor Consistency for Noisy Labels
    • Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification
    • Multi-class Token Transformer for Weakly Supervised Semantic Segmentation
    • Multidimensional Belief Quantification for Label-Efficient Meta-Learning
    • Multi-Label Classification With Partial Annotations Using Class-Aware Selective Loss
    • Multi-Label Iterated Learning for Image Classification With Label Ambiguity
    • Multi-Marginal Contrastive Learning for Multi-Label Subcellular Protein Localization
    • Mutual Quantization for Cross-Modal Search With Noisy Labels
    • Not All Labels Are Equal_ Rationalizing the Labeling Costs for Training Object Detection
    • Not All Relations Are Equal_ Mining Informative Labels for Scene Graph Generation.pdf
    • On Learning Contrastive Representations for Learning With Noisy Labels
    • Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling
    • Part-Based Pseudo Label Refinement for Unsupervised Person Re-Identification
    • PLAD_ Learning To Infer Shape Programs With Pseudo-Labels and Approximate Distributions
    • PNP_ Robust Learning From Noisy Labels by Probabilistic Noise Prediction
    • Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples
    • Replacing Labeled Real-Image Datasets With Auto-Generated Contours
    • Safe-Student for Safe Deep Semi-Supervised Learning With Unseen-Class Unlabeled Data
    • Scalable Penalized Regression for Noise Detection in Learning With Noisy Labels
    • Selective-Supervised Contrastive Learning With Noisy Labels
    • Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation With Reliable Voted Pseudo Labels
    • Self-Taught Metric Learning without Labels
    • Semi-Supervised Learning of Semantic Correspondence With Pseudo-Labels
    • Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
    • The Devil Is in the Labels_ Noisy Label Correction for Robust Scene Graph Generation
    • The Devil Is in the Margin_ Margin-Based Label Smoothing for Network Calibration
    • The Neurally-Guided Shape Parser_ Grammar-Based Labeling of 3D Shape Regions With Approximate Inference
    • Towards Data-Free Model Stealing in a Hard Label Setting
    • TWIST_ Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation
    • Undoing the Damage of Label Shift for Cross-Domain Semantic Segmentation
    • UniCon_ Combating Label Noise Through Uniform Selection and Contrastive Learning
    • Unified Contrastive Learning in Image-Text-Label Space
    • Unimodal-Concentrated Loss_ Fully Adaptive Label Distribution Learning for Ordinal Regression
    • Use All The Labels_ A Hierarchical Multi-Label Contrastive Learning Framework
    • Which Images To Label for Few-Shot Medical Landmark Detection_

CVPR2022

Label 相关

CVPR2022

• A Dual Weighting Label Assignment Scheme for Object Detection
• ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
• ADeLA_ Automatic Dense Labeling With Attention for Viewpoint Shift in Semantic Segmentation
• Back to Reality_ Weakly-Supervised 3D Object Detection With Shape-Guided Label Enhancement
• BoostMIS_ Boosting Medical Image Semi-Supervised Learning With Adaptive Pseudo Labeling and Informative Active Annotation
• Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition
• DASO_ Distribution-Aware Semantics-Oriented Pseudo-Label for Imbalanced Semi-Supervised Learning
• Debiased Learning From Naturally Imbalanced Pseudo-Labels
• Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement
• Dist-PU_ Positive-Unlabeled Learning From a Label Distribution Perspective
• Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation
• FedCorr_ Multi-Stage Federated Learning for Label Noise Correction
• Few-Shot Incremental Learning for Label-to-Image Translation
• Few-Shot Learning With Noisy Labels
• Improving Segmentation of the Inferior Alveolar Nerve Through Deep Label Propagation
• Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment
• Incremental Learning in Semantic Segmentation From Image Labels
• Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation
• Interactive Multi-Class Tiny-Object Detection
• Label Matching Semi-Supervised Object Detection
• Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification
• Label, Verify, Correct_ A Simple Few Shot Object Detection Method
• Label-Only Model Inversion Attacks via Boundary Repulsion
• Large Loss Matters in Weakly Supervised Multi-Label Classification
• Large-Scale Pre-training for Person Re-identification with Noisy Labels
• Learning Fair Classifiers With Partially Annotated Group Labels
• Learning From Pixel-Level Noisy Label_ A New Perspective for Light Field Saliency Detection
• Learning To Detect Mobile Objects From LiDAR Scans Without Labels
• Learning To Imagine_ Diversify Memory for Incremental Learning Using Unlabeled Data
• Learning With Neighbor Consistency for Noisy Labels
• Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification
• Multi-class Token Transformer for Weakly Supervised Semantic Segmentation
• Multidimensional Belief Quantification for Label-Efficient Meta-Learning
• Multi-Label Classification With Partial Annotations Using Class-Aware Selective Loss
• Multi-Label Iterated Learning for Image Classification With Label Ambiguity
• Multi-Marginal Contrastive Learning for Multi-Label Subcellular Protein Localization
• Mutual Quantization for Cross-Modal Search With Noisy Labels
• Not All Labels Are Equal_ Rationalizing the Labeling Costs for Training Object Detection
• Not All Relations Are Equal_ Mining Informative Labels for Scene Graph Generation.pdf
• On Learning Contrastive Representations for Learning With Noisy Labels
• Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling
• Part-Based Pseudo Label Refinement for Unsupervised Person Re-Identification
• PLAD_ Learning To Infer Shape Programs With Pseudo-Labels and Approximate Distributions
• PNP_ Robust Learning From Noisy Labels by Probabilistic Noise Prediction
• Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples
• Replacing Labeled Real-Image Datasets With Auto-Generated Contours
• Safe-Student for Safe Deep Semi-Supervised Learning With Unseen-Class Unlabeled Data
• Scalable Penalized Regression for Noise Detection in Learning With Noisy Labels
• Selective-Supervised Contrastive Learning With Noisy Labels
• Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation With Reliable Voted Pseudo Labels
• Self-Taught Metric Learning without Labels
• Semi-Supervised Learning of Semantic Correspondence With Pseudo-Labels
• Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
• The Devil Is in the Labels_ Noisy Label Correction for Robust Scene Graph Generation
• The Devil Is in the Margin_ Margin-Based Label Smoothing for Network Calibration
• The Neurally-Guided Shape Parser_ Grammar-Based Labeling of 3D Shape Regions With Approximate Inference
• Towards Data-Free Model Stealing in a Hard Label Setting
• TWIST_ Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation
• Undoing the Damage of Label Shift for Cross-Domain Semantic Segmentation
• UniCon_ Combating Label Noise Through Uniform Selection and Contrastive Learning
• Unified Contrastive Learning in Image-Text-Label Space
• Unimodal-Concentrated Loss_ Fully Adaptive Label Distribution Learning for Ordinal Regression
• Use All The Labels_ A Hierarchical Multi-Label Contrastive Learning Framework
• Which Images To Label for Few-Shot Medical Landmark Detection_

A Dual Weighting Label Assignment Scheme for Object Detection

ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification

ADeLA_ Automatic Dense Labeling With Attention for Viewpoint Shift in Semantic Segmentation

Back to Reality_ Weakly-Supervised 3D Object Detection With Shape-Guided Label Enhancement

BoostMIS_ Boosting Medical Image Semi-Supervised Learning With Adaptive Pseudo Labeling and Informative Active Annotation

Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition

DASO_ Distribution-Aware Semantics-Oriented Pseudo-Label for Imbalanced Semi-Supervised Learning

Debiased Learning From Naturally Imbalanced Pseudo-Labels

Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement

Dist-PU_ Positive-Unlabeled Learning From a Label Distribution Perspective

Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation

FedCorr_ Multi-Stage Federated Learning for Label Noise Correction

Few-Shot Incremental Learning for Label-to-Image Translation

Few-Shot Learning With Noisy Labels

Improving Segmentation of the Inferior Alveolar Nerve Through Deep Label Propagation

Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment

Incremental Learning in Semantic Segmentation From Image Labels

Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation

Interactive Multi-Class Tiny-Object Detection

Label Matching Semi-Supervised Object Detection

Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification

Label, Verify, Correct_ A Simple Few Shot Object Detection Method

Label-Only Model Inversion Attacks via Boundary Repulsion

Large Loss Matters in Weakly Supervised Multi-Label Classification

Large-Scale Pre-training for Person Re-identification with Noisy Labels

Learning Fair Classifiers With Partially Annotated Group Labels

Learning From Pixel-Level Noisy Label_ A New Perspective for Light Field Saliency Detection

Learning To Detect Mobile Objects From LiDAR Scans Without Labels

Learning To Imagine_ Diversify Memory for Incremental Learning Using Unlabeled Data

Learning With Neighbor Consistency for Noisy Labels

Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification

Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

Multidimensional Belief Quantification for Label-Efficient Meta-Learning

Multi-Label Classification With Partial Annotations Using Class-Aware Selective Loss

Multi-Label Iterated Learning for Image Classification With Label Ambiguity

Multi-Marginal Contrastive Learning for Multi-Label Subcellular Protein Localization

Mutual Quantization for Cross-Modal Search With Noisy Labels

Not All Labels Are Equal_ Rationalizing the Labeling Costs for Training Object Detection

Not All Relations Are Equal_ Mining Informative Labels for Scene Graph Generation.pdf

On Learning Contrastive Representations for Learning With Noisy Labels

Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling

Part-Based Pseudo Label Refinement for Unsupervised Person Re-Identification

PLAD_ Learning To Infer Shape Programs With Pseudo-Labels and Approximate Distributions

PNP_ Robust Learning From Noisy Labels by Probabilistic Noise Prediction

Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples

Replacing Labeled Real-Image Datasets With Auto-Generated Contours

Safe-Student for Safe Deep Semi-Supervised Learning With Unseen-Class Unlabeled Data

Scalable Penalized Regression for Noise Detection in Learning With Noisy Labels

Selective-Supervised Contrastive Learning With Noisy Labels

Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation With Reliable Voted Pseudo Labels

Self-Taught Metric Learning without Labels

Semi-Supervised Learning of Semantic Correspondence With Pseudo-Labels

Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

The Devil Is in the Labels_ Noisy Label Correction for Robust Scene Graph Generation

The Devil Is in the Margin_ Margin-Based Label Smoothing for Network Calibration

The Neurally-Guided Shape Parser_ Grammar-Based Labeling of 3D Shape Regions With Approximate Inference

Towards Data-Free Model Stealing in a Hard Label Setting

TWIST_ Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation

Undoing the Damage of Label Shift for Cross-Domain Semantic Segmentation

UniCon_ Combating Label Noise Through Uniform Selection and Contrastive Learning

Unified Contrastive Learning in Image-Text-Label Space

Unimodal-Concentrated Loss_ Fully Adaptive Label Distribution Learning for Ordinal Regression

Use All The Labels_ A Hierarchical Multi-Label Contrastive Learning Framework

Which Images To Label for Few-Shot Medical Landmark Detection_