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Computer Vision – ECCV 2020

Overview of attention for book
Cover of 'Computer Vision – ECCV 2020'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 MessyTable: Instance Association in Multiple Camera Views
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    Chapter 4 HTML: A Parametric Hand Texture Model for 3D Hand Reconstruction and Personalization
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    Chapter 6 Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
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    Chapter 7 Towards Real-Time Multi-Object Tracking
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    Chapter 8 A Balanced and Uncertainty-Aware Approach for Partial Domain Adaptation
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    Chapter 16 From Shadow Segmentation to Shadow Removal
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    Chapter 17 Diverse and Admissible Trajectory Forecasting Through Multimodal Context Understanding
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    Chapter 18 CONFIG: Controllable Neural Face Image Generation
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    Chapter 19 Single View Metrology in the Wild
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    Chapter 20 Procedure Planning in Instructional Videos
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    Chapter 25 HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction
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    Chapter 29 Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement
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    Chapter 30 Semi-supervised Learning with a Teacher-Student Network for Generalized Attribute Prediction
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    Chapter 33 Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
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    Chapter 34 DataMix: Efficient Privacy-Preserving Edge-Cloud Inference
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    Chapter 35 Neural Re-rendering of Humans from a Single Image
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    Chapter 39 CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing
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    Chapter 41 Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting
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    Chapter 42 Making Affine Correspondences Work in Camera Geometry Computation
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    Chapter 43 Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces
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    Chapter 44 Foley Music: Learning to Generate Music from Videos
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    Chapter 45 Contrastive Multiview Coding
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

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Article details
Title
Computer Vision – ECCV 2020
Published by
arXiv, January 2020
DOI 10.1007/978-3-030-58621-8
ISBNs
978-3-03-058620-1, 978-3-03-058621-8
Authors

Benjamin Biggs, Oliver Boyne, James Charles, Andrew Fitzgibbon, Roberto Cipolla

Editors

Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 September 2026.
All research outputs
#1,867,501
of 33,834,589 outputs
Outputs from arXiv
#32,855
of 1,482,665 outputs
Outputs of similar age
#35,880
of 523,224 outputs
Outputs of similar age from arXiv
#533
of 19,689 outputs
Altmetric has tracked 33,834,589 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,482,665 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 523,224 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 19,689 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.