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|a 10.1007/978-3-030-58548-8
|2 doi
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|a (OCoLC)1225892183
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|a European Conference on Computer Vision
|n (16th :
|d 2020 :
|c Online)
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|a Computer vision -- ECCV 2020 :
|b 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings.
|n Part IV /
|c Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm (eds.).
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3 |
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|a ECCV 2020
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1 |
|a Cham :
|b Springer,
|c 2020.
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| 300 |
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|a 1 online resource (xliii, 817 pages) :
|b illustrations (some color)
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
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|b PDF
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|a Lecture notes in computer science ;
|v 12349
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| 490 |
1 |
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|a LNCS sublibrary: SL 6, Image processing, computer vision, pattern recognition, and graphics
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|a International conference proceedings.
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|a The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
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|a Includes author index.
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|a Online resource; title from PDF title page (SpringerLink, viewed January 20, 2021).
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|a Intro -- Foreword -- Preface -- Organization -- Contents -- Part IV -- Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors -- 1 Introduction -- 2 Related Work -- 2.1 Object Detector Basics -- 3 Approach -- 3.1 Creating a Universal Adversarial Patch -- 4 Crafting Attacks in the Digital World -- 4.1 Evaluation of Digital Attacks -- 5 Physical World Attacks -- 5.1 Printed Posters -- 5.2 Paper Dolls -- 6 Wearable Adversarial Examples -- 7 Conclusion -- References -- TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images -- 1 Introduction
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| 505 |
8 |
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|a 2 Related Works -- 2.1 Image-to-Image Translation -- 2.2 Image Style Transfer -- 2.3 Single Image Generative Models -- 3 Method -- 3.1 Network Architecture -- 3.2 Loss Functions -- 3.3 Implementation Details -- 4 Experiments -- 4.1 Baselines -- 4.2 Evaluation Metrics -- 4.3 Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Semi-Siamese Training for Shallow Face Learning -- 1 Introduction -- 2 Related Work -- 2.1 Deep Face Recognition -- 2.2 Low-Shot Face Recognition -- 2.3 Self-supervised Learning -- 3 The Proposed Approach -- 3.1 Shallow Face Learning Problem
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| 505 |
8 |
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|a 3.2 Semi-Siamese Training -- 4 Experiments -- 4.1 Datasets and Experimental Settings -- 4.2 Ablation Study -- 4.3 SST with Various Loss Functions -- 4.4 SST with Various Network Architectures -- 4.5 SST on Deep Data Learning -- 4.6 Pretrain and Finetune -- 5 Conclusions -- References -- GAN Slimming: All-in-One GAN Compression by a Unified Optimization Framework -- 1 Introduction -- 2 Related Works -- 2.1 Deep Model Compression -- 2.2 GAN Compression -- 3 The GAN Slimming Framework -- 3.1 The Unified Optimization Form -- 3.2 End-to-End Optimization -- 3.3 Algorithm Implementation -- 4 Experiments
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| 505 |
8 |
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|a 4.1 Unpaired Image Translation with CycleGAN -- 4.2 Ablation Study -- 4.3 Real-World Application: CartoonGAN -- 5 Conclusion -- A Image Generation with SNGAN -- References -- Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Framework -- 3.2 Skeleton Points Interaction Learning Module -- 3.3 Multi-head Mechanism -- 3.4 Skeleton Point Convolution -- 4 Experiments -- 4.1 Ablation Study -- 4.2 Comparison with the State of the Art -- 4.3 Failure Case -- 5 Conclusion -- References
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| 505 |
8 |
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|a Binarized Neural Network for Single Image Super Resolution -- 1 Introduction -- 2 Related Work -- 2.1 Single Image Super Resolution -- 2.2 Quantitative Model -- 3 Proposed Approach -- 3.1 Motivation -- 3.2 Quantization of Weights -- 3.3 Quantization of Activations -- 3.4 Binary Super Resolution Network -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementations -- 4.3 Evaluation -- 4.4 Model Analysis -- 5 Conclusions -- References -- Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Position-Sensitive Self-attention
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| 650 |
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|a Computer vision
|v Congresses.
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| 650 |
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|a Optical data processing.
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| 650 |
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|a Machine learning.
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| 650 |
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|a Pattern perception.
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|a Computer vision
|2 fast
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|a Application software
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|a Computers, Special purpose
|2 fast
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|a Machine learning
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|a Optical data processing
|2 fast
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|a Pattern perception
|2 fast
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|a Electronic books.
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|a proceedings (reports)
|2 aat
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|a Conference papers and proceedings
|2 fast
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|a Conference papers and proceedings.
|2 lcgft
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| 655 |
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|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Vedaldi, Andrea,
|e editor
|1 https://orcid.org/0000-0003-1374-2858
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| 700 |
1 |
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|a Bischof, Horst,
|e editor
|1 https://orcid.org/0000-0002-9096-6671
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| 700 |
1 |
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|a Brox, Thomas,
|e editor
|1 https://orcid.org/0000-0002-6282-8861
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| 700 |
1 |
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|a Frahm, Jan-Michael,
|e editor.
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| 758 |
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|i has work:
|a Computer vision -- ECCV 2020 Part IV (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFvYjB3MWCFH3kc9xVQCkP
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
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|i Printed edition:
|z 9783030585471
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783030585495
|
| 830 |
|
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|a Lecture notes in computer science ;
|v 12349.
|
| 830 |
|
0 |
|a LNCS sublibrary.
|n SL 6,
|p Image processing, computer vision, pattern recognition, and graphics.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-58548-8
|y Click for online access
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| 903 |
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|a SPRING-COMP2020
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| 994 |
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|a 92
|b HCD
|