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Computer Vision – ECCV 2022

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Table of Contents

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    Book Overview
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    Chapter 1 Efficient One-Stage Video Object Detection by Exploiting Temporal Consistency
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    Chapter 2 Leveraging Action Affinity and Continuity for Semi-supervised Temporal Action Segmentation
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    Chapter 3 Spotting Temporally Precise, Fine-Grained Events in Video
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    Chapter 4 Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation
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    Chapter 5 Efficient Video Transformers with Spatial-Temporal Token Selection
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    Chapter 6 Long Movie Clip Classification with State-Space Video Models
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    Chapter 7 Prompting Visual-Language Models for Efficient Video Understanding
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    Chapter 8 Asymmetric Relation Consistency Reasoning for Video Relation Grounding
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    Chapter 9 Self-supervised Social Relation Representation for Human Group Detection
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    Chapter 10 K -centered Patch Sampling for Efficient Video Recognition
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    Chapter 11 A Deep Moving-Camera Background Model
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    Chapter 12 GraphVid : It only Takes a Few Nodes to Understand a Video
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    Chapter 13 Delta Distillation for Efficient Video Processing
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    Chapter 14 MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
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    Chapter 15 COMPOSER : Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality
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    Chapter 16 E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context
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    Chapter 17 TDViT: Temporal Dilated Video Transformer for Dense Video Tasks
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    Chapter 18 Semi-supervised Learning of Optical Flow by Flow Supervisor
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    Chapter 19 Flow Graph to Video Grounding for Weakly-Supervised Multi-step Localization
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    Chapter 20 Deep 360 $$^\circ $$ ∘ Optical Flow Estimation Based on Multi-projection Fusion
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    Chapter 21 MaCLR: Motion-Aware Contrastive Learning of Representations for Videos
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    Chapter 23 Frozen CLIP Models are Efficient Video Learners
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    Chapter 24 PIP: Physical Interaction Prediction via Mental Simulation with Span Selection
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    Chapter 25 Panoramic Vision Transformer for Saliency Detection in 360 $$^\circ $$ ∘ Videos
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    Chapter 26 Bayesian Tracking of Video Graphs Using Joint Kalman Smoothing and Registration
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    Chapter 27 Motion Sensitive Contrastive Learning for Self-supervised Video Representation
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    Chapter 28 Dynamic Temporal Filtering in Video Models
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    Chapter 29 Tip-Adapter: Training-Free Adaption of CLIP for Few-Shot Classification
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    Chapter 30 Temporal Lift Pooling for Continuous Sign Language Recognition
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    Chapter 31 MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes
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    Chapter 32 SiRi: A Simple Selective Retraining Mechanism for Transformer-Based Visual Grounding
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    Chapter 34 TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts
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    Chapter 35 SeqTR: A Simple Yet Universal Network for Visual Grounding
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    Chapter 37 FashionViL: Fashion-Focused Vision-and-Language Representation Learning
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    Chapter 38 Weakly Supervised Grounding for VQA in Vision-Language Transformers
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    Chapter 39 Automatic Dense Annotation of Large-Vocabulary Sign Language Videos
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    Chapter 40 MILES: Visual BERT Pre-training with Injected Language Semantics for Video-Text Retrieval
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    Chapter 41 GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval
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    Chapter 42 A Simple and Robust Correlation Filtering Method for Text-Based Person Search
Attention for Chapter 5: Efficient Video Transformers with Spatial-Temporal Token Selection
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Chapter title
Efficient Video Transformers with Spatial-Temporal Token Selection
Chapter number 5
Book title
Computer Vision – ECCV 2022
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-19833-5_5
Book ISBNs
978-3-03-119832-8, 978-3-03-119833-5
Authors

Wang, Junke, Yang, Xitong, Li, Hengduo, Liu, Li, Wu, Zuxuan, Jiang, Yu-Gang

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 17%
Student > Master 6 10%
Student > Bachelor 4 7%
Student > Doctoral Student 3 5%
Researcher 3 5%
Other 2 3%
Unknown 30 52%
Readers by discipline Count As %
Computer Science 20 34%
Engineering 3 5%
Mathematics 1 2%
Agricultural and Biological Sciences 1 2%
Biochemistry, Genetics and Molecular Biology 1 2%
Other 0 0%
Unknown 32 55%