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by Vittorio Murino, Bodo Rosenhahn, Michael Ying Yang
Multimodal Scene Understanding
Cover image
Title page
Table of Contents
Copyright
List of Contributors
Chapter 1: Introduction to Multimodal Scene Understanding
Abstract
1.1. Introduction
1.2. Organization of the Book
References
Chapter 2: Deep Learning for Multimodal Data Fusion
Abstract
2.1. Introduction
2.2. Related Work
2.3. Basics of Multimodal Deep Learning: VAEs and GANs
2.4. Multimodal Image-to-Image Translation Networks
2.5. Multimodal Encoder–Decoder Networks
2.6. Experiments
2.7. Conclusion
References
Chapter 3: Multimodal Semantic Segmentation: Fusion of RGB and Depth Data in Convolutional Neural Networks
Abstract
3.1. Introduction
3.2. Overview
3.3. Methods
3.4. Results and Discussion
3.5. Conclusion
References
Chapter 4: Learning Convolutional Neural Networks for Object Detection with Very Little Training Data
Abstract
Acknowledgement
4.1. Introduction
4.2. Fundamentals
4.3. Related Work
4.4. Traffic Sign Detection
4.5. Localization
4.6. Clustering
4.7. Dataset
4.8. Experiments
4.9. Conclusion
References
Chapter 5: Multimodal Fusion Architectures for Pedestrian Detection
Abstract
Acknowledgement
5.1. Introduction
5.2. Related Work
5.3. Proposed Method
5.4. Experimental Results and Discussion
5.5. Conclusion
References
Chapter 6: Multispectral Person Re-Identification Using GAN for Color-to-Thermal Image Translation
Abstract
Acknowledgements
6.1. Introduction
6.2. Related Work
6.3. ThermalWorld Dataset
6.4. Method
6.5. Evaluation
6.6. Conclusion
References
Chapter 7: A Review and Quantitative Evaluation of Direct Visual–Inertial Odometry
Abstract
7.1. Introduction
7.2. Related Work
7.3. Background: Nonlinear Optimization and Lie Groups
7.4. Background: Direct Sparse Odometry
7.5. Direct Sparse Visual–Inertial Odometry
7.6. Calculating the Relative Jacobians
7.7. Results
7.8. Conclusion
References
Chapter 8: Multimodal Localization for Embedded Systems: A Survey
Abstract
8.1. Introduction
8.2. Positioning Systems and Perception Sensors
8.3. State of the Art on Localization Methods
8.4. Multimodal Localization for Embedded Systems
8.5. Application Domains
8.6. Conclusion
References
Chapter 9: Self-Supervised Learning from Web Data for Multimodal Retrieval
Abstract
Acknowledgements
9.1. Introduction
9.2. Related Work
9.3. Multimodal Text–Image Embedding
9.4. Text Embeddings
9.5. Benchmarks
9.6. Retrieval on InstaCities1M and WebVision Datasets
9.7. Retrieval in the MIRFlickr Dataset
9.8. Comparing the Image and Text Embeddings
9.9. Visualizing CNN Activation Maps
9.10. Visualizing the Learned Semantic Space with t-SNE
9.11. Conclusions
References
Chapter 10: 3D Urban Scene Reconstruction and Interpretation from Multisensor Imagery
Abstract
10.1. Introduction
10.2. Pose Estimation for Wide-Baseline Image Sets
10.3. Dense 3D Reconstruction
10.4. Scene Classification
10.5. Scene and Building Decomposition
10.6. Building Modeling
10.7. Conclusion and Future Work
References
Chapter 11: Decision Fusion of Remote-Sensing Data for Land Cover Classification
Abstract
11.1. Introduction
11.2. Proposed Framework
11.3. Use Case #1: Hyperspectral and Very High Resolution Multispectral Imagery for Urban Material Discrimination
11.4. Use Case #2: Urban Footprint Detection
11.5. Final Outlook and Perspectives
References
Chapter 12: Cross-modal Learning by Hallucinating Missing Modalities in RGB-D Vision
Abstract
12.1. Introduction
12.2. Related Work
12.3. Generalized Distillation with Multiple Stream Networks
12.4. Experiments
12.5. Conclusions and Future Work
References
Index
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