Computational knowledge vision : the first footprints /

This book, 'Computational Knowledge Vision,' authored by Wenbo Zheng and Fei-Yue Wang, explores the integration of knowledge and vision in artificial intelligence (AI). It delves into computational frameworks that enhance machine learning and understanding of visual data, much like human p...

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Bibliographic Details
Main Author:	ZHENG, WENBO
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Wang, Fei-Yue
Format:	eBook
Language:	English
Published:	[S.l.] : Academic Press, 2024.
Subjects:	Computer vision. Information visualization. Vision par ordinateur. Visualisation de l'information. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Computational Knowledge Vision
Copyright
Contents
Biography
Preface
References
Acknowledgments
1 Start from here
1.1 Motivation for writing this book
1.2 Organization of the book
References
1 Computational Knowledge Vision framework
2 Reviewing the past enables us to learn
2.1 "Information is not knowledge"
2.2 "Knowledge is power"
2.3 Toward Computational Knowledge Vision
2.3.1 When humanoids inspire computer vision
2.3.1.1 Neural network model
2.3.1.2 Meta-learning model
2.3.2 When combinatorial mathematics meets computer vision
2.3.3 When natural language supplements computer vision
2.3.4 "Standing on the shoulders of giants"
References
3 Computational Knowledge Vision
3.1 What is the goal of the Computational Knowledge Vision?
3.2 Why is Computational Knowledge Vision appropriate?
3.3 What is the logic of Computational Knowledge Vision by which it can be carried out?
3.3.1 Structurized knowledge
3.3.2 Knowledge projection and conditioned feedback
3.3.3 Reasoning, understanding, and representation
References
2 Computational Knowledge Vision solution
4 Low vision: Computational Knowledge Vision for edge detection
4.1 Introduction
4.2 Proposed approach
4.2.1 Differential evolution algorithm
4.2.2 Differential-evolutionary-based generative adversarial networks
4.2.3 The process and structure of DEGAN
4.3 Experiments and results
4.3.1 Ablation study
4.3.2 Comparison with state-of-the-art methods
4.3.2.1 BSDS500 dataset
4.3.2.2 NYUD dataset
4.4 Conclusions
References
5 Middle vision: Computational Knowledge Vision for visual translation
5.1 Introduction
5.2 Just-noticeable-difference model
5.2.1 Luminance adaptation
5.2.2 Contrast masking
5.3 Proposed approach.
5.3.1 Just-noticeable-difference model of our approach
5.3.2 Network formulation
5.3.3 Network architecture
5.4 Experiments and results
5.4.1 Ablation study
5.4.2 Comparison with state-of-the-art methods
5.4.2.1 Qualitative evaluation
5.4.2.2 Quantitative evaluation
5.4.2.3 Domain adaptation
5.5 Conclusions
References
6 Middle vision: Computational Knowledge Vision for jointly face recognition
6.1 Introduction
6.2 Proposed approach
6.2.1 Problem definition
6.2.2 Knowledge graph construction and representation
6.2.3 Network-based representation learning
6.2.4 Knowledge-based representation learning
6.2.5 Meta-learning model
6.2.6 Meta-continual learning model
6.3 Experiments and results
6.3.1 Experimental setup
6.3.2 Comparison with the state-of-the-art methods
Sketch face recognition
Caricature face recognition
Cartoon face recognition
6.3.3 Discussion on the generalization ability
6.4 Conclusions
References
7 High vision: Computational Knowledge Vision for visual reasoning
7.1 Introduction
7.2 Proposed approach
7.2.1 Building knowledge bases
Multimodal Attributes Encoding
Multimodal Attributes Recovery
7.2.2 Mutual modulation model
Visual Modulation
Language Modulation
Cascaded Modulation
7.2.3 Knowledge-based key-value memory network
Key Hashing
Key Addressing and Value Reading
Hop Iterations
7.2.4 Knowledge-based representation learning
7.3 Experiments and results
7.3.1 Dataset description
7.3.2 Experiment setup
7.3.3 Comparison with state-of-the-art methods
7.3.4 Ablation study
7.3.5 Discussion about key hashing in key-value memory networks
7.4 Conclusions
References
3 Computational Knowledge Vision application
8 Affective computing: Computational Knowledge Vision for depression detection.
8.1 Introduction
8.2 Proposed approach
8.2.1 Basic notation
8.2.2 Multimodal attention mechanisms
8.2.3 Temporal convolution networks
8.2.4 Knowledge-based representation learning
8.2.5 Objective function
8.3 Experiments and results
8.3.1 Dataset description
DAIC-WOZ dataset
SMD dataset
8.3.2 Experiment setup
Evaluation measures
Implementation details
8.3.3 Comparison with state-of-the-art methods
Comparison on the DAIC-WOZ dataset
Comparison on the SMD dataset
8.3.4 Ablation study
8.4 Conclusions
References
9 Medical computing: Computational Knowledge Vision for COVID-19 detection
9.1 Introduction
9.2 Multimodal dataset construction
9.2.1 Dataset creation and structure
9.2.2 Dataset comparison
9.3 Proposed approach
9.3.1 Problem definition
9.3.2 Network representation learning
9.3.3 Data augmentation
9.3.4 Self-knowledge distillation
9.3.5 Training methods
9.4 Experiments and results
9.4.1 Experimental settings
9.4.2 The results of our model
9.4.3 Comparison with state-of-the-art methods
COVID-19 diagnosis from pneumonia cases on our dataset
COVID-19 cases diagnosis on COVID v2.0 dataset
9.5 Conclusions
References
10 Medical computing: Computational Knowledge Vision for medical visual reasoning
10.1 Introduction
10.2 Proposed approach
10.2.1 Problem setup
10.2.2 Knowledge graph construction and representation
10.2.3 Feature fusion network
10.2.4 Knowledge-based representation learning
10.2.5 Meta-learning model
10.3 Experiments and results
10.3.1 Dataset description
10.3.2 Experiment setup
10.3.3 Comparison with state-of-the-art methods
10.3.4 Discussion about other similar methods
Summary about comparison and discussion experiments
10.3.5 Error analysis
10.4 Conclusions
References.
Index
Back Cover.