"Advances in Visual Computing" - Information and Links:

Advances in Visual Computing

15th International Symposium, ISVC 2020, San Diego, CA, USA, October 5-7, 2020, Proceedings, Part II

"Advances in Visual Computing" is published by Springer International Publishing AG in 2020 - Cham, it has 1 pages and the language of the book is English.


“Advances in Visual Computing” Metadata:

  • Title: Advances in Visual Computing
  • Authors:
  • Language: English
  • Number of Pages: 1
  • Publisher: ➤  Springer International Publishing AG
  • Publish Date:
  • Publish Location: Cham

“Advances in Visual Computing” Subjects and Themes:

Edition Specifications:

  • Weight: 1.217
  • Pagination: xxviii, 777

Edition Identifiers:

AI-generated Review of “Advances in Visual Computing”:


"Advances in Visual Computing" Description:

Open Data:

Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Object Recognition/Detection/Categorization -- Few-Shot Image Recognition with Manifolds -- 1 Introduction -- 2 Proposed Approach -- 2.1 Estimating Novel-Class Prototypes -- 2.2 Classification Using Absorbing Markov Chain -- 3 Experiments and Discussions -- 3.1 Dataset Description -- 3.2 Effects of Varying the Number of Classes and Samples -- 3.3 Parameter Sensitivity Studies -- 4 Conclusions -- References -- A Scale-Aware YOLO Model for Pedestrian Detection -- 1 Introduction -- 2 Related Works -- 3 Our Detection Algorithm -- 3.1 Overview of Our Framework -- 3.2 YOLOv3 -- 3.3 Architecture of SA YOLOv3 -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Quantitative Comparison -- 4.3 Qualitative Comparison -- 5 Conclusion -- References -- Image Categorization Using Agglomerative Clustering Based Smoothed Dirichlet Mixtures -- 1 Introduction -- 2 Methodology -- 2.1 Mixture of Smoothed Dirichlet Distributions -- 2.2 Model Learning -- 2.3 KL-distance Based Smoothed Dirichlet Mixture Model -- 2.4 Image Categorization Framework: Agglomerative Clustering -- 3 Experiments and Results -- 4 Conclusion -- References -- SAT-CNN: A Small Neural Network for Object Recognition from Satellite Imagery -- 1 Introduction -- 2 Related Works -- 2.1 Satellite Imagery Datasets -- 3 Methodology -- 3.1 Chip Generation -- 3.2 Data Augmentation -- 3.3 Balancing Methods -- 3.4 SAT-CNN Architecture -- 4 Experiments and Results -- 4.1 Data Augmentation -- 4.2 Balancing Methods -- 4.3 Transfer Learning -- 4.4 Evaluation -- 5 Conclusion -- References -- Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-Grained Visual Categorization -- 1 Introduction -- 2 Related Work -- 2.1 Data Augmentation -- 2.2 Fine-Grained Visual Categorization -- 2.3 Attention

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