Computer Vision – ECCV 2022

Table of Contents

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   Book Overview
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   Chapter 1 Cross-domain Ensemble Distillation for Domain Generalization
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   Chapter 2 Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels
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   Chapter 3 Hyperspherical Learning in Multi-Label Classification
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   Chapter 4 When Active Learning Meets Implicit Semantic Data Augmentation
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   Chapter 5 VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
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   Chapter 6 Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization
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   Chapter 7 Hierarchical Semi-supervised Contrastive Learning for Contamination-Resistant Anomaly Detection
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   Chapter 8 Tracking by Associating Clips
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    Chapter 9 RealPatch: A Statistical Matching Framework for Model Patching with Real Samples
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    Chapter 10 Background-Insensitive Scene Text Recognition with Text Semantic Segmentation
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    Chapter 11 Semantic Novelty Detection via Relational Reasoning
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    Chapter 12 Improving Closed and Open-Vocabulary Attribute Prediction Using Transformers
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    Chapter 13 Training Vision Transformers with only 2040 Images
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    Chapter 14 Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection
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    Chapter 15 TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs
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    Chapter 16 Automatic Check-Out via Prototype-Based Classifier Learning from Single-Product Exemplars
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    Chapter 17 Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain
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    Chapter 18 Photo-realistic Neural Domain Randomization
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    Chapter 19 Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning
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    Chapter 20 Tailoring Self-Supervision for Supervised Learning
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    Chapter 21 Difficulty-Aware Simulator for Open Set Recognition
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    Chapter 22 Few-Shot Class-Incremental Learning from an Open-Set Perspective
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    Chapter 23 FOSTER: Feature Boosting and Compression for Class-Incremental Learning
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    Chapter 24 Visual Knowledge Tracing
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    Chapter 25 S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning
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    Chapter 27 VSA: Learning Varied-Size Window Attention in Vision Transformers
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    Chapter 28 Unbiased Manifold Augmentation for Coarse Class Subdivision
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    Chapter 29 DenseHybrid: Hybrid Anomaly Detection for Dense Open-Set Recognition
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    Chapter 30 Rethinking Confidence Calibration for Failure Prediction
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    Chapter 31 Uncertainty-Guided Source-Free Domain Adaptation
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    Chapter 32 Should All Proposals Be Treated Equally in Object Detection?
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    Chapter 33 ViP: Unified Certified Detection and Recovery for Patch Attack with Vision Transformers
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    Chapter 34 incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection
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    Chapter 35 IGFormer: Interaction Graph Transformer for Skeleton-Based Human Interaction Recognition
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    Chapter 36 PRIME: A Few Primitives Can Boost Robustness to Common Corruptions
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    Chapter 37 Rotation Regularization Without Rotation
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    Chapter 38 Towards Accurate Open-Set Recognition via Background-Class Regularization
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    Chapter 39 In Defense of Image Pre-Training for Spatiotemporal Recognition
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    Chapter 40 Augmenting Deep Classifiers with Polynomial Neural Networks
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    Chapter 41 Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
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    Chapter 42 Online Task-free Continual Learning with Dynamic Sparse Distributed Memory