Intro
Preface
Organization
Contents
Attention Systems
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields
1 Introduction
2 Second-Order Channel Attention with Varying Receptive Fields
2.1 Second-Order Channel Attention
2.2 Dilated Convolutions
2.3 Compression Through Dilations
2.4 Model Overview
3 Experimental Results
3.1 Datasets
3.2 Implementation
3.3 Evaluation
4 Conclusion
References
MARL: Multimodal Attentional Representation Learning for Disease Prediction
1 Introduction

2 Related Work
3 Multimodal Attentional Representation Learning
3.1 Preprocessing
3.2 Lung Segmentation with Spatial Fuzzy C-Means
3.3 Patient Biological Data Enrichment
3.4 Multimodal Representation Learning
4 Experimental Work
4.1 IPF Lung Disease Dataset
4.2 Regression of IPF Lung Disease Progression
4.3 IPF Disease Status Classification
5 Conclusion
References
Object Localization with Attribute Preference Based on Top-Down Attention
1 Introduction
2 Related Work
3 Architecture
3.1 Deep Attribute Network
3.2 Top-Down Attention
4 Experiments

4.1 Datasets
4.2 Pointing Game Evaluation
4.3 Experiment 1: Attribute Localization
4.4 Experiment 2: Joint Attribute-Object Localization
5 Conclusion
References
See the Silence: Improving Visual-Only Voice Activity Detection by Optical Flow and RGB Fusion
1 Introduction
2 Related Work
3 Our Approach
4 Experimental Evaluation
4.1 Dataset and Training
4.2 Results
5 Conclusion
References
Classification and Detection
Score to Learn: A Comparative Analysis of Scoring Functions for Active Learning in Robotics
1 Introduction
2 Related Work

2.1 Deep Learning Based Object Detection
2.2 Weakly-Supervised Learning of Object Detection
3 Methods
3.1 Overview of the Pipeline
3.2 Scoring Functions
4 Experiments
4.1 Experimental Setup
4.2 Results Analysis on Pascal VOC
4.3 Results Analysis on iCWT
5 Discussion
References
Enhancing the Performance of Image Classification Through Features Automatically Learned from Depth-Maps
1 Introduction
2 Literature Review on Indoor-Outdoor Image Classification
3 Methodology
3.1 Data Set
3.2 Feature Extraction
3.3 Unsupervised Learning-Based Analysis

3.4 Supervised Learning Based Analysis
4 Results and Discussion
4.1 Experimental Setup
4.2 Results of the Unsupervised Learning Based Analysis
4.3 Results of the Supervised Learning Based Analysis
5 Conclusions and Future Work
References
Object Detection on TPU Accelerated Embedded Devices
1 Introduction
2 Related Works
2.1 Mobile Object Detectors
2.2 Embedded Deep Learning
3 Methodology
3.1 Framework and Quantization
3.2 Datasets
3.3 Transfer Learning
4 Evaluation
4.1 Inference Time
4.2 Accuracy
4.3 Versatility and Training Speed