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Computer Vision – ECCV 2020

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Cover of 'Computer Vision – ECCV 2020'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 EfficientFCN: Holistically-Guided Decoding for Semantic Segmentation
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    Chapter 2 GroSS: Group-Size Series Decomposition for Grouped Architecture Search
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    Chapter 3 Efficient Adversarial Attacks for Visual Object Tracking
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    Chapter 4 Globally-Optimal Event Camera Motion Estimation
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    Chapter 5 Weakly-Supervised Learning of Human Dynamics
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    Chapter 6 Journey Towards Tiny Perceptual Super-Resolution
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    Chapter 7 What Makes Fake Images Detectable? Understanding Properties that Generalize
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    Chapter 8 Embedding Propagation: Smoother Manifold for Few-Shot Classification
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    Chapter 9 Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
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    Chapter 10 High-Fidelity Synthesis with Disentangled Representation
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    Chapter 11 PL$$_1$$P - Point-Line Minimal Problems Under Partial Visibility in Three Views
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    Chapter 12 Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification
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    Chapter 13 Learning Canonical Representations for Scene Graph to Image Generation
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    Chapter 14 Adversarial Robustness on In- and Out-Distribution Improves Explainability
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    Chapter 15 Deformable Style Transfer
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    Chapter 16 Aligning Videos in Space and Time
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    Chapter 17 Neural Wireframe Renderer: Learning Wireframe to Image Translations
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    Chapter 18 RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax
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    Chapter 19 Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
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    Chapter 20 Determining the Relevance of Features for Deep Neural Networks
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    Chapter 21 Weakly Supervised Semantic Segmentation with Boundary Exploration
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    Chapter 22 GANHopper: Multi-hop GAN for Unsupervised Image-to-Image Translation
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    Chapter 23 DOPE: Distillation of Part Experts for Whole-Body 3D Pose Estimation in the Wild
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    Chapter 24 Multi-view Adaptive Graph Convolutions for Graph Classification
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    Chapter 25 Instance Adaptive Self-training for Unsupervised Domain Adaptation
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    Chapter 26 Weight Decay Scheduling and Knowledge Distillation for Active Learning
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    Chapter 27 HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs
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    Chapter 28 Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning
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    Chapter 29 Geometry Constrained Weakly Supervised Object Localization
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    Chapter 30 Duality Diagram Similarity: A Generic Framework for Initialization Selection in Task Transfer Learning
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    Chapter 31 OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering
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    Chapter 32 Mining Self-similarity: Label Super-Resolution with Epitomic Representations
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    Chapter 33 AE-OT-GAN: Training GANs from Data Specific Latent Distribution
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    Chapter 34 Null-Sampling for Interpretable and Fair Representations
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    Chapter 35 Guiding Monocular Depth Estimation Using Depth-Attention Volume
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    Chapter 36 Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping
  38. Altmetric Badge
    Chapter 37 Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer
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    Chapter 38 BézierSketch: A Generative Model for Scalable Vector Sketches
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    Chapter 39 Semantic Relation Preserving Knowledge Distillation for Image-to-Image Translation
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    Chapter 40 Domain Adaptation Through Task Distillation
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    Chapter 41 PatchAttack: A Black-Box Texture-Based Attack with Reinforcement Learning
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    Chapter 42 More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning
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    Chapter 43 Extending and Analyzing Self-supervised Learning Across Domains
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    Chapter 44 Multi-source Open-Set Deep Adversarial Domain Adaptation
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    Chapter 45 Neural Batch Sampling with Reinforcement Learning for Semi-supervised Anomaly Detection
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    Chapter 46 LEMMA: A Multi-view Dataset for L Earning Multi-agent Multi-task Activities
Attention for Chapter 4: Globally-Optimal Event Camera Motion Estimation
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  • Average Attention Score compared to outputs of the same age
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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Chapter title
Globally-Optimal Event Camera Motion Estimation
Chapter number 4
Book title
Computer Vision – ECCV 2020
Published in
arXiv, November 2020
DOI 10.1007/978-3-030-58574-7_4
Book ISBNs
978-3-03-058573-0, 978-3-03-058574-7
Authors

Xin Peng, Yifu Wang, Ling Gao, Laurent Kneip, Peng, Xin, Wang, Yifu, Gao, Ling, Kneip, Laurent

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 20%
Student > Master 11 19%
Student > Doctoral Student 3 5%
Student > Bachelor 2 3%
Student > Postgraduate 2 3%
Other 1 2%
Unknown 28 47%
Readers by discipline Count As %
Computer Science 22 37%
Engineering 7 12%
Economics, Econometrics and Finance 1 2%
Unknown 29 49%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 09 March 2022.
All research outputs
#15,175,585
of 24,093,053 outputs
Outputs from arXiv
#295,510
of 1,020,419 outputs
Outputs of similar age
#230,021
of 415,950 outputs
Outputs of similar age from arXiv
#9,828
of 34,583 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,020,419 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 66% 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 415,950 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34,583 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.