TY  - BOOK
AU  - Vedaldi,Andrea
AU  - Bischof,Horst
AU  - Brox,Thomas
AU  - Frahm,Jan-Michael
ED  - European Conference on Computer Vision
TI  - Computer vision -- ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings
T2  - Lecture Notes in Computer Science
SN  - 9783030585204
AV  - TA1634 
U1  - 006.3/7 23
PY  - 2020///
CY  - Cham
PB  - Springer
KW  - Computer vision
KW  - Congresses
KW  - Optical data processing
KW  - Artificial intelligence
KW  - Pattern perception
KW  - Application software
KW  - Data protection
KW  - Artificial Intelligence
KW  - Vision par ordinateur
KW  - Congrès
KW  - Traitement optique de l'information
KW  - Intelligence artificielle
KW  - Perception des structures
KW  - Logiciels d'application
KW  - Protection de l'information (Informatique)
KW  - artificial intelligence
KW  - aat
KW  - fast
KW  - Congress
KW  - proceedings (reports)
KW  - Conference papers and proceedings
KW  - lcgft
KW  - Actes de congrès
KW  - rvmgf
N1  - Intro -- Foreword -- Preface -- Organization -- Contents -- Part XVII -- Class-Wise Dynamic Graph Convolution for Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Preliminaries -- 3.2 Overall Framework -- 3.3 Class-Wise Dynamic Graph Convolution Module -- 3.4 Loss Function -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparisons with State-of-the-Arts -- 5 Conclusions -- References -- Character-Preserving Coherent Story Visualization -- 1 Introduction -- 2 Related Work; 2.1 GAN-based Text-to-Image Synthesis -- 2.2 Evaluation Metrics of Image Generation -- 3 Character-Preserving Coherent Story Visualization -- 3.1 Overview -- 3.2 Story and Context Encoder -- 3.3 Figure-Ground Aware Generation -- 3.4 Loss Function -- 3.5 Fréchet Story Distance -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Dataset -- 4.3 Baselines -- 4.4 Qualitative Comparison -- 4.5 Quantitative Comparison -- 4.6 Architecture Search -- 4.7 FSD Analysis -- 5 Conclusions -- References -- GINet: Graph Interaction Network for Scene Parsing -- 1 Introduction -- 2 Related Work; 3 Approach -- 3.1 Framework of Graph Interaction Network (GINet) -- 3.2 Graph Interaction Unit -- 3.3 Semantic Context Loss -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Experiments on Pascal-Context -- 4.4 Experiments on COCO Stuff -- 4.5 Experiments on ADE20K -- 5 Conclusion -- References -- Tensor Low-Rank Reconstruction for Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Tensor Generation Module -- 3.3 Tensor Reconstruction Module -- 3.4 Global Pooling Module -- 3.5 Network Details -- 3.6 Relation to Previous Approaches; 4 Experiments -- 4.1 Implementation Details -- 4.2 Results on Different Datasets -- 4.3 Ablation Study -- 4.4 Further Discussion -- 5 Conclusion -- References -- Attentive Normalization -- 1 Introduction -- 2 Related Work -- 3 The Proposed Attentive Normalization -- 3.1 Background on Feature Normalization -- 3.2 Background on Feature Attention -- 3.3 Attentive Normalization -- 4 Experiments -- 4.1 Ablation Study -- 4.2 Image Classification in ImageNet-1000 -- 4.3 Object Detection and Segmentation in COCO -- 5 Conclusion -- References -- Count- and Similarity-Aware R-CNN for Pedestrian Detection; 1 Introduction -- 2 Related Work -- 3 Baseline Two-Stage Detection Framework -- 4 Our Approach -- 4.1 Detection Branch -- 4.2 Count-and-Similarity Branch -- 4.3 Inference -- 5 Experiments -- 5.1 Datasets and Evaluation Metrics -- 5.2 Implementation Details -- 5.3 CityPersons Dataset -- 5.4 CrowdHuman Dataset -- 5.5 Results on Person Instance Segmentation -- 6 Conclusion -- References -- TRADI: Tracking Deep Neural Network Weight Distributions -- 1 Introduction -- 2 TRAcking of the Weight DIstribution (TRADI) -- 2.1 Notations and Hypotheses
N2  - 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
UR  - https://link.springer.com/10.1007/978-3-030-58520-4
ER  -