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Computer Vision – ECCV 2022

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Cover of 'Computer Vision – ECCV 2022'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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
Attention for Chapter 14: DnA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment
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Chapter title
DnA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment
Chapter number 14
Book title
Computer Vision – ECCV 2022
Published by
Springer, Cham, October 2022
DOI 10.1007/978-3-031-20044-1_14
Book ISBNs
978-3-03-120043-4, 978-3-03-120044-1
Authors

Ziyu Jiang, Tianlong Chen, Xuxi Chen, Yu Cheng, Luowei Zhou, Lu Yuan, Ahmed Awadallah, Zhangyang Wang, Jiang, Ziyu, Chen, Tianlong, Chen, Xuxi, Cheng, Yu, Zhou, Luowei, Yuan, Lu, Awadallah, Ahmed, Wang, Zhangyang

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Student > Master 3 33%
Unknown 3 33%
Readers by discipline Count As %
Computer Science 4 44%
Neuroscience 1 11%
Unknown 4 44%

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