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...
Đã lưu trong:
| Tác giả chính: | |
|---|---|
| Tác giả khác: | |
| Định dạng: | Điện tử eBook |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
Cambridge, MA :
Academic Press,
2024.
|
| Những chủ đề: | |
| Truy cập trực tuyến: | ScienceDirect |
| Các nhãn: |
Thêm thẻ
Không có thẻ, Là người đầu tiên thẻ bản ghi này!
|
MARC
| LEADER | 00000cam a2200000 a 4500 | ||
|---|---|---|---|
| 001 | on1452959353 | ||
| 003 | OCoLC | ||
| 005 | 20260915095236.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 240823s2024 xx o 000 0 eng d | ||
| 040 | |a YDX |b eng |e pn |c YDX |d OCLCO |d OPELS |d OCLCO |d UKAHL |d UKMGB |d OCLCL |d SFB |d N$T |d ORMDA |d OCLCF |d OCLCO |d WAU |d OCLCQ | ||
| 015 | |a GBC477928 |2 bnb | ||
| 016 | 7 | |a 300198048 |2 Uk | |
| 020 | |a 9780443216183 |q (electronic bk.) | ||
| 020 | |a 0443216185 |q (electronic bk.) | ||
| 020 | |z 9780443216190 | ||
| 020 | |z 0443216193 | ||
| 024 | 8 | |a CIPO000110012 | |
| 035 | |a (OCoLC)1452959353 | ||
| 050 | 4 | |a TA1634 | |
| 082 | 0 | 4 | |a 006.3/7 |2 23/eng/20240903 |1 https://id.oclc.org/worldcat/ddc/E3ccrkvy8KfTbP6HGmrBtHxYkQ |
| 100 | 1 | |a ZHENG, WENBO. | |
| 245 | 1 | 0 | |a Computational knowledge vision : |b the first footprints / |c Wenbo Zheng, Fei-Yue Wang. |
| 260 | |a Cambridge, MA : |b Academic Press, |c 2024. | ||
| 300 | |a 1 online resource (xix, 256 pages) : |b illustrations | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a computer |2 rdamedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 340 | |p illustration |0 http://rdaregistry.info/termList/IllusContent/1014 |2 rdaill | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a Index -- Back Cover. | |
| 520 | |a 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 perception and reasoning. By discussing various levels of vision from low to high, the book addresses edge detection, visual translation, face recognition, and visual reasoning. It also applies these concepts to practical domains such as affective computing for depression detection and medical computing for COVID-19 detection. Aimed at researchers and practitioners in AI and computer vision, the book offers insights into the evolving role of data and knowledge in advancing AI technologies. |7 Generated by AI | ||
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Information visualization. | |
| 650 | 6 | |a Vision par ordinateur. |0 (CaQQLa)201-0074889 | |
| 650 | 6 | |a Visualisation de l'information. |0 (CaQQLa)201-0371241 | |
| 650 | 7 | |a Computer vision |2 fast |0 (OCoLC)fst00872687 | |
| 650 | 7 | |a Information visualization |2 fast |0 (OCoLC)fst00973185 | |
| 700 | 1 | |a Wang, Fei-Yue. | |
| 776 | 0 | 8 | |i Print version: |z 0443216193 |z 9780443216190 |w (OCoLC)1423134104 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443216190 |