Computer Vision – ECCV 2022

Table of Contents

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   Book Overview
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   Chapter 1 tSF: Transformer-Based Semantic Filter for Few-Shot Learning
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   Chapter 2 Adversarial Feature Augmentation for Cross-domain Few-Shot Classification
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   Chapter 3 Constructing Balance from Imbalance for Long-Tailed Image Recognition
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   Chapter 4 On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond
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   Chapter 5 Few-Shot Video Object Detection
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   Chapter 6 Worst Case Matters for Few-Shot Recognition
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   Chapter 7 Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification
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   Chapter 8 Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation
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    Chapter 9 Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
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    Chapter 10 Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
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    Chapter 11 CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition
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    Chapter 12 Few-Shot Class-Incremental Learning for 3D Point Cloud Objects
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    Chapter 13 Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
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    Chapter 14 DnA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment
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    Chapter 15 Learning Instance and Task-Aware Dynamic Kernels for Few-Shot Learning
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    Chapter 16 Open-World Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding
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    Chapter 17 Few-Shot Classification with Contrastive Learning
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    Chapter 18 Time-rEversed DiffusioN tEnsor Transformer: A New TENET of Few-Shot Object Detection
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    Chapter 19 Self-Promoted Supervision for Few-Shot Transformer
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    Chapter 20 Few-Shot Object Counting and Detection
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    Chapter 21 Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
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    Chapter 22 Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations
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    Chapter 23 Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object Detection
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    Chapter 24 Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation
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    Chapter 25 Improving Few-Shot Learning Through Multi-task Representation Learning Theory
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    Chapter 26 Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
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    Chapter 27 Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments
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    Chapter 28 Temporal and Cross-modal Attention for Audio-Visual Zero-Shot Learning
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    Chapter 29 HM: Hybrid Masking for Few-Shot Segmentation
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    Chapter 30 TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot Learning
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    Chapter 31 Kernel Relative-prototype Spectral Filtering for Few-Shot Learning
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    Chapter 32 “This Is My Unicorn, Fluffy”: Personalizing Frozen Vision-Language Representations
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    Chapter 33 CLOSE: Curriculum Learning on the Sharing Extent Towards Better One-Shot NAS
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    Chapter 34 Streamable Neural Fields
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    Chapter 35 Gradient-Based Uncertainty for Monocular Depth Estimation
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    Chapter 36 Online Continual Learning with Contrastive Vision Transformer
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    Chapter 37 CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution
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    Chapter 38 EAutoDet: Efficient Architecture Search for Object Detection
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    Chapter 39 A Max-Flow Based Approach for Neural Architecture Search
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    Chapter 40 OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses
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    Chapter 41 ERA: Enhanced Rational Activations
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    Chapter 42 Convolutional Embedding Makes Hierarchical Vision Transformer Stronger