
Pattern Recognition. ICPR International Workshops and Challenges
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Content
- Intro
- Foreword by General Chairs
- Preface
- Challenges
- ICPR Organization
- Towards AI Ethics and Explainability (ICPR EDL-AI Workshop Plenary Talk)
- Contents - Part III
- EDL-AI - Explainable Deep Learning/AI
- Preface
- Organization
- General Chairs
- Program Committee Chairs
- Program Committee
- Publication Chairs
- Panel Chair
- Additional Reviewers
- A Multi-layered Approach for Tailored Black-Box Explanations
- 1 Introduction
- 2 Context and Requirements
- 2.1 Context
- 2.2 Requirements
- 3 From Contexts to Explanations
- 3.1 From Context to Requirements
- 3.2 From Requirements to Technical Options
- 4 IBEX at Work: Application to Case Studies
- 4.1 Explanations to Enhance Trust
- 4.2 Explanations to Take Actions
- 5 Related Works
- 6 Conclusion
- References
- Post-hoc Explanation Options for XAI in Deep Learning: The Insight Centre for Data Analytics Perspective
- 1 Introduction
- 2 Post-hoc Factual Explanations: Images
- 2.1 The Method: COLE
- 2.2 Results: Factual Image-Based Explanations
- 3 Post-hoc Counterfactual Explanations: Images
- 3.1 The Method: PIECE
- 3.2 Results: Counterfactual Image-Based Explanations
- 4 Post-hoc Semi-factual Explanations: Images
- 4.1 Method and Results: PIECE for Semi-factuals
- 5 Post-hoc,Counterfactual Explanations: Time-Series
- 5.1 The Method: Native-Guide for Time-Series Counterfactuals
- 5.2 Results: Native-Guide for Counterfactuals
- 6 Future Directions
- References
- Expert Level Evaluations for Explainable AI (XAI) Methods in the Medical Domain
- 1 Introduction
- 2 Eye-Tracking Experiments and Data Collection
- 2.1 Data Collection Protocol
- 3 Explainable AI Methods
- 3.1 SIDU
- 3.2 GRAD-CAM
- 4 Comparison Metrics for XAI Methods
- 4.1 Area Under ROC Curve (AUC)
- 4.2 Kullback-Leibler Divergence (KL-DIV)
- 5 Experimental Evaluation and Results
- 5.1 Training CNN Models
- 5.2 Results and Discussion
- 6 Concluding Remarks
- References
- Samples Classification Analysis Across DNN Layers with Fractal Curves
- 1 Introduction
- 2 Previous Works
- 2.1 Visualization for the Interpretation of Deep Neural Networks
- 2.2 Hilbert Curve in Information Visualization
- 3 Proposed Method
- 3.1 Domain Level
- 3.2 Abstraction Level
- 3.3 Technique Level
- 3.4 Algorithms Level
- 4 Experimental Protocol
- 4.1 Scenarios
- 4.2 Implementation and Execution Infrastructure
- 5 Results and Discussion
- 6 Conclusion
- References
- Random Forest Model and Sample Explainer for Non-experts in Machine Learning - Two Case Studies
- 1 Introduction
- 2 Integrated Random Forest Model and Sample Explainer - RFEX
- 2.1 RFEX Model Explainer
- 2.2 RFEX Sample Explainer
- 3 RFEX Explanation of Early Mortality Prediction for COVID-19 Patients
- 3.1 RFEX Model Explainer for the Prediction of COVID-19 Mortality from the Data for Day 0
- 3.2 RFEX Model Explainer for Early Prediction of COVID-19 Mortality from the Data for Day -7
- 4 RFEX Explanation of Classification of Human Nervous System Cell Type Clusters
- 4.1 RFEX Model Explainer Applied to JCVI Data
- 4.2 RFEX Sample Explainer Applied to JCVI Data
- References
- Jointly Optimize Positive and Negative Saliencies for Black Box Classifiers
- 1 Introduction
- 2 Related Works
- 2.1 Class Activation Map
- 2.2 Backpropagation-Based Method
- 2.3 Perturbation-Based Method
- 2.4 Mask-Based Method
- 3 Joint Mask Method
- 3.1 Baseline Works
- 3.2 Deletion and Negative Saliency
- 3.3 Integration of Preservation and Deletion Masks
- 4 Experiments
- 4.1 Comparison with the Other Perspectives
- 4.2 Comparison with Other Saliency Methods
- 4.3 Sanity Check
- 5 Conclusions
- References
- Low Dimensional Visual Attributes: An Interpretable Image Encoding
- 1 Introduction
- 2 Related Work
- 3 Low Dimensional Visual Attributes
- 4 Label-Limited Classification Evaluation
- 5 Interpretability
- 5.1 Learned Part and Prototypes
- 5.2 Crowd-Sourced Experiments
- 6 Conclusion
- References
- Explainable 3D-CNN for Multiple Sclerosis Patients Stratification
- 1 Introduction
- 2 Materials and Methods
- 2.1 Population, Data Acquisition and Image Processing
- 2.2 Network Architecture
- 2.3 Training, Validation and Testing
- 2.4 CNN Visualization
- 2.5 LRP Heatmaps Analysis
- 3 Results
- 4 Discussion
- 5 Conclusions
- References
- Visualizing the Effect of Semantic Classes in the Attribution of Scene Recognition Models
- 1 Introduction
- 2 Method
- 2.1 Preliminaries
- 2.2 Score Deviation
- 2.3 Score Deviation Map
- 2.4 Class-Wise Statistics
- 2.5 Particularization to Scene Recognition
- 3 Experimental Results
- 3.1 Implementation Details
- 3.2 Score Deviation Maps
- 3.3 Relevant, Irrelevant and Distracting Semantic Classes
- 4 Conclusions
- References
- The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks
- 1 Introduction
- 1.1 Related Work
- 1.2 Contribution
- 2 Methods
- 2.1 Activation Sparsity Definition
- 2.2 Activation Sparsity Visualisation
- 2.3 Activation Sparsity Regularisation
- 2.4 Experimental Design
- 3 Results
- 3.1 Relationship Between Overfitting and Activation Sparsity
- 3.2 Spatial Analysis of Activation Sparstiy
- 3.3 Activation Sparsity Regularisation Results
- 3.4 Activation Sparsity in Common Deep Architectures
- 4 Conclusion
- References
- Remove to Improve?
- 1 Introduction
- 2 Previous Work
- 3 Background and Definitions
- 4 Methodology
- 5 Experiments
- 5.1 Class-Wise Accuracy Changes
- 5.2 Identification of Filters for Each Class
- 5.3 Class-Wise Filter Overlap and Semantic Similarity
- 5.4 Analysis of Groups
- 5.5 Nearest Neighbours
- 5.6 Class-Wise Correlation Between Pruned Filters
- 6 Discussion and Conclusions
- References
- Explaining How Deep Neural Networks Forget by Deep Visualization
- 1 Introduction
- 2 Related Work
- 3 Approach
- 3.1 Catastrophic Forgetting Dissector - CFD
- 3.2 Critical Freezing
- 4 Experiments
- 5 Conclusion and Future Work
- References
- Deep Learning for Astrophysics, Understanding the Impact of Attention on Variability Induced by Parameter Initialization
- 1 Introduction
- 2 Related Work
- 3 Proposed Architecture and Performance
- 3.1 -PhysNet Architecture
- 3.2 Experiments
- 4 Understanding the Impact of Dual Attention
- 5 Conclusion
- References
- A General Approach to Compute the Relevance of Middle-Level Input Features
- 1 Introduction
- 2 Related Works
- 3 Middle-Level Relevance
- 3.1 Decoder by Super-Pixel Segmentation
- 3.2 Decoder by Sparse Dictionary Learning Methods
- 4 Experimental Assessment
- 4.1 Qualitative Results
- 4.2 Quantitative Evaluation
- 5 Conclusions
- References
- Evaluation of Interpretable Association Rule Mining Methods on Time-Series in the Maritime Domain
- 1 Introduction
- 2 Related Work
- 3 Foundations
- 3.1 Classification
- 3.2 Data Preprocessing
- 3.3 Association Rule Mining-ARM
- 4 Methods
- 4.1 Scalable Bayesian Rule Lists-SBRL
- 4.2 Rule-Based Regularization Method
- 4.3 Gini Regularization Method
- 5 Evaluation Metrics
- 6 Experimental Results
- 6.1 Datasets
- 6.2 Experimental Setup
- 6.3 Results
- 7 Conclusion and Future Work
- References
- Anchors vs Attention: Comparing XAI on a Real-Life Use Case
- 1 Introduction
- 2 Related Works
- 2.1 EXplainable Artificial Intelligence
- 2.2 Evaluate Explanations
- 3 Experiments
- 3.1 LEGO
- 3.2 YELP
- 4 Evaluating Explanations
- 4.1 Quantitative Analysis
- 4.2 Qualitative Analysis
- 5 Conclusion
- References
- Explanation-Driven Characterization of Android Ransomware
- 1 Introduction
- 2 Background on Android
- 2.1 Android Ransomware
- 3 Explanation Methods
- 4 Ransomware Detection and Explanations
- 4.1 Detector Design
- 4.2 Explaining Android Ransomware
- 5 Experimental Analysis
- 5.1 Setting
- 5.2 Preliminary Evaluation
- 5.3 Explanation Analysis
- 6 Contributions, Limitations, and Future Work
- References
- Reliability of eXplainable Artificial Intelligence in Adversarial Perturbation Scenarios
- 1 Introduction
- 2 Related Works
- 3 Methods
- 4 Results
- 5 Conclusions
- References
- AI Explainability. A Bridge Between Machine Vision and Natural Language Processing
- 1 Introduction
- 2 Background
- 3 Link Between Image and Text in Explainability
- 4 Potential Benefits to NLP Community
- 4.1 Word-Sense Disambiguation
- 4.2 Text Argumentation Theory
- 4.3 Sentiment Analysis
- 4.4 Topical Modelling
- 4.5 Automatic Textual Summarization
- 5 Conclusion
- References
- Recursive Division of Image for Explanation of Shallow CNN Models
- 1 Introduction
- 2 Shallow vs Deep Architectures
- 3 Explanation Methods
- 4 Recursive Division
- 5 Experimental Modelling
- 5.1 UEC FOOD 100
- 5.2 UEC FOOD 256
- 5.3 Crack Dataset
- 5.4 Performance
- 6 Conclusions
- References
- EgoApp 2020 - 2nd Workshop on Applications of Egocentric Vision 2020
- The Second Workshop on Applications of Egocentric Vision (EgoApp)
- Workshop Description
- Organization
- Organizing Committee
- Program Committee
- Camera Ego-Positioning Using Sensor Fusion and Complementary Method
- 1 Introduction
- 2 Related Work
- 2.1 Visual Ego-Positioning
- 2.2 Sensor Fusion of Camera and IMU
- 2.3 Complementary Ego-Positioning
- 3 Sensor Fusion of Camera and IMU
- 3.1 Method
- 3.2 Camera-IMU System Calibration
- 4 Complementary Ego-Positioning
- 4.1 3D Point Registration
- 4.2 Complementary Fusion
- 5 Experiments
- 6 Conclusion
- References
- ATSal: An Attention Based Architecture for Saliency Prediction in 360 Videos
- 1 Introduction
- 2 Related Work
- 2.1 2D Dynamic Saliency Models
- 2.2 360 Heuristic Approaches
- 2.3 360 Data-Driven Approaches
- 3 Proposed Model
- 3.1 Attention Mechanism
- 3.2 Expert Models
- 3.3 Loss Function for the Attention Stream
- 4 Experiments
- 4.1 Experimental Setup
- 4.2 Results
- 4.3 Performance Study
- 5 Conclusion
- References
- Rescue Dog Action Recognition by Integrating Ego-Centric Video, Sound and Sensor Information
- 1 Introduction
- 2 Related Work
- 2.1 Third-Person Activity Recognition
- 2.2 First-Person Activity Recognition
- 2.3 Dog-Centric Activity Modeling
- 3 Dataset
- 3.1 11 Dog Activity Classes
- 4 Method
- 4.1 The Detail of the Image/Sound/Sensor-Based Four-Stream CNN
- 5 Experiments
- 5.1 Selection of Sound Stream Network
- 5.2 Selection of Sound Window Size
- 5.3 Selection of Sensor Data Network
- 5.4 Selection of Sensor Window Size
- 5.5 Experiments by Integration of All Modalities
- 6 Conclusions
- References
- Understanding Event Boundaries for Egocentric Activity Recognition from Photo-Streams
- 1 Introduction
- 2 Related Work
- 3 Activity Recognition from Event Boundaries
- 3.1 Boundaries Detection
- 3.2 Event-Based Activity Recognition
- 4 Experimental Setup
- 4.1 Dataset
- 4.2 Implementation
- 4.3 Evaluation Metrics
- 5 Results
- 5.1 Generic Vs Personalized Learning
- 5.2 Random Forest Based Models Vs. Deep Models
- 5.3 LSTM Vs. BLSTM Temporal Models
- 5.4 Event Clustering Vs. No Segmentation
- 6 Conclusions
- References
- Egomap: Hierarchical First-Person Semantic Mapping
- 1 Introduction
- 2 Relationship with Previous Work
- 2.1 Related Approaches
- 2.2 Datasets
- 3 Model
- 3.1 Hierarchical Map
- 3.2 View Representation
- 4 Motion Analysis for Transition Detection
- 5 Inference
- 5.1 View Inference via Recursive Bayes
- 5.2 Station Inference
- 6 Learning
- 6.1 View Update
- 6.2 View Creation
- 6.3 Station Update
- 6.4 Station Creation
- 6.5 Transition Matrix Updates
- 7 Experiments and Metrics
- 7.1 Dataset
- 7.2 Dictionary Learning and Parameter Tuning
- 7.3 Map Evaluation
- 7.4 Baseline and Ablation
- 8 Results
- 8.1 Visualisation
- 8.2 Quantitative Evaluation
- 9 Conclusions
- References
- ETTAC 2020 - Workshop on Eye Tracking Techniques, Applications and Challenges
- Preface
- Organization
- General Chairs
- Program Committee
- Additional Reviewers
- Ultrasound for Gaze Estimation
- 1 Introduction
- 2 Materials and Methods
- 2.1 Bench-Top Setup
- 2.2 Data Analysis
- 3 Results
- 4 Discussion
- References
- Synthetic Gaze Data Augmentation for Improved User Calibration
- 1 Introduction
- 2 Related Works
- 3 Working Framework
- 3.1 Image Databases
- 3.2 Image Conditioning
- 3.3 Network Architecture
- 3.4 Implementation Details
- 4 Subject Calibration
- 5 Experiments
- 6 Results
- 6.1 Number of Training Images and Regressor Estimation
- 6.2 U2Eyes and ImageNet Methods
- 7 Conclusions
- References
- Eye Movement Classification with Temporal Convolutional Networks
- 1 Introduction
- 2 Related Work
- 2.1 Threshold-Based Methods
- 2.2 Probabilistic Methods
- 2.3 Data-Driven Methods
- 3 Model Architecture
- 4 Evaluation
- 4.1 Materials
- 4.2 Dataset
- 4.3 Feature Extraction
- 4.4 Metrics
- 4.5 Training and Evaluation
- 5 Results
- 6 Discussion
- 7 Conclusion
- References
- A Web-Based Eye Tracking Data Visualization Tool
- 1 Introduction
- 2 Related Work
- 3 Data Handling
- 3.1 Data Validation
- 3.2 Clustering
- 3.3 Heatmap Data
- 3.4 Caching
- 4 Visualization Techniques
- 4.1 AOI Timeline
- 4.2 Gaze Plot
- 4.3 Heatmap
- 4.4 Scarf Plot
- 4.5 General Interactions
- 5 Web Application Architecture
- 6 Use Case: Metro Map of Antwerp
- 7 Discussion and Limitations
- 7.1 Front-End and Back-End Decisions
- 7.2 Performance
- 7.3 Uploading
- 7.4 Interactions
- 7.5 Interface
- 7.6 Views
- 8 Conclusion and Future Work
- References
- Influence of Peripheral Vibration Stimulus on Viewing and Response Actions
- 1 Introduction
- 2 Experiment
- 2.1 Visual Stimuli
- 2.2 Experimental Procedure
- 2.3 Subjects
- 3 Results
- 3.1 Percentage Correct of the Task of Peripheral Field of Vision Viewing
- 3.2 Hierarchical Bayesian Modelling
- 3.3 Analysis of Microsaccades
- 4 Discussion
- 5 Conclusion
- References
- Judging Qualification, Gender, and Age of the Observer Based on Gaze Patterns When Looking at Faces
- 1 Introduction
- 2 Methods and Material
- 3 Results
- 3.1 Observer Related Variables
- 3.2 Image Properties
- 3.3 Classification
- 4 Summary
- References
- Gaze Stability During Ocular Proton Therapy: Quantitative Evaluation Based on Eye Surface Surveillance Videos
- 1 Introduction
- 2 Material and Methods
- 2.1 Patient Data
- 2.2 Automatic Pupil Detection Algorithm
- 2.3 Validation
- 2.4 Evaluation of Pupil Position Stability
- 3 Results
- 3.1 Validation
- 3.2 Evaluation of Pupil Position Stability
- 4 Discussion
- References
- Predicting Reading Speed from Eye-Movement Measures
- 1 Introduction
- 1.1 Eye Movements and Reading Speed
- 1.2 Effects of Inter-letter Spacing Modulation on Reading
- 2 Methods
- 2.1 Participants
- 2.2 Apparatus
- 2.3 Stimuli and Experimental Procedure
- 2.4 Data Analysis
- 3 Results
- 3.1 Correlation Analyses
- 3.2 Regression Using All Eye-Movement Measures (Set 1)
- 3.3 Regression Using Different Subsets of Eye-Movement Measures (Set 2-4)
- 4 Discussion
- References
- Investigating the Effect of Inter-letter Spacing Modulation on Data-Driven Detection of Developmental Dyslexia Based on Eye-Movement Correlates of Reading: A Machine Learning Approach
- 1 Introduction
- 2 Materials and Methods
- 2.1 Materials
- 2.2 Methods
- 3 Results
- 4 Discussion
- References
- FAPER - International Workshop on Fine Art Pattern Extraction and Recognition
- Preface
- Organization
- Chairs
- Program Committee
- A Brief Overview of Deep Learning Approaches to Pattern Extraction and Recognition in Paintings and Drawings
- 1 Introduction
- 2 Main Datasets and Deep Learning Approaches
- 2.1 Datasets
- 2.2 Deep Learning Approaches
- 3 Main Research Trends
- 3.1 Artwork Attribute Prediction
- 3.2 Object Recognition and Detection
- 3.3 Content Generation
- 4 Concluding Remarks and Future Directions
- References
- Iconographic Image Captioning for Artworks
- 1 Introduction
- 2 Related Work
- 3 Experimental Setup
- 3.1 Iconclass Caption Dataset
- 3.2 Vision-Language Model
- 4 Results
- 4.1 Quantitative Results
- 4.2 Qualitative Analysis
- 5 Conclusion
- References
- Semantic Analysis of Cultural Heritage Data: Aligning Paintings and Descriptions in Art-Historic Collections
- 1 Introduction
- 2 Related Work
- 3 Challenges for Image and Text Alignment in the Cultural Heritage Domain
- 4 Alignment Approach
- 4.1 Word Encodings
- 4.2 Vocabulary Augmentation
- 4.3 Neural Style Transfer
- 5 Results and Discussion
- 5.1 Experimental Setup
- 5.2 Datasets
- 5.3 Results
- 6 Conclusion
- References
- Insights from a Large-Scale Database of Material Depictions in Paintings
- 1 Introduction
- 2 Dataset
- 3 Using Computer Vision to Analyze Paintings
- 3.1 Extracting Polygon Segments with Interactive Segmentation
- 3.2 Detecting Materials in Unlabeled Paintings
- 4 Using Paintings to Build Better Recognition Systems
- 4.1 Learning Robust Cues for Finegrained Fabric Classification
- 4.2 Benchmarking Unsupervised Domain Adaptation
- 5 Conclusion
- References
- An Analysis of the Transfer Learning of Convolutional Neural Networks for Artistic Images
- 1 Introduction
- 2 Related Work
- 2.1 Deep Transfer Learning for Art Classification Problems
- 2.2 Deep Convolutional Neural Network Understanding
- 2.3 Datasets
- 3 Analyzing CNNs Trained for Art Classification Tasks
- 3.1 From Natural to Art Images
- 3.2 Training from Scratch
- 3.3 Classification Performance
- 3.4 Quantitative Evaluation of the CNNs Modification
- 3.5 From One Art Dataset to Another
- 4 Conclusion
- References
- Handwriting Classification for the Analysis of Art-Historical Documents
- 1 Introduction
- 2 Related Work
- 3 Method
- 3.1 Handwriting Synthesis
- 3.2 Handwriting Classification Networks
- 4 Results and Discussion
- 4.1 Datasets
- 4.2 Experimental Setup
- 4.3 Results on the GANwriting Dataset
- 4.4 Results on the 5CHPT Dataset
- 4.5 Results on the WPI Dataset
- 4.6 Classification of an Additional Unseen Class
- 5 Conclusion
- References
- Color Space Exploration of Paintings Using a Novel Probabilistic Divergence
- 1 Introduction
- 2 The Color Theory
- 3 RGB Color Space
- 4 Probabilistic Divergence Measure
- 5 A Novel Probabilistic Divergence Measure
- 6 Experiments and Results
- 6.1 Classic Masters
- 6.2 Modern Masters
- 6.3 Abstract Masters
- 7 Related Works
- 8 Conclusion
- References
- Identifying Centres of Interest in Paintings Using Alignment and Edge Detection
- 1 Introduction
- 2 Case Studies
- 3 Step I. Finding the Original Image
- 4 Step II. Aligning the Painting and the Original
- 5 Step III. Micro-transformations
- 6 Step IV. Deconstructing Possible Meanings
- 7 A Second Case Study
- 8 Conclusions
- References
- Attention-Based Multi-modal Emotion Recognition from Art
- 1 Introduction
- 2 Related Work
- 2.1 Modalities in Emotion Recognition
- 2.2 Emotion Recognition from Art
- 2.3 Proposed Multi-modal Fusion Model
- 2.4 Image Feature Representation
- 2.5 Text Feature Representation
- 2.6 Attention Layer
- 2.7 Classification Layer
- 3 Experiment and Results
- 3.1 Dataset
- 3.2 Training Details
- 3.3 Baseline
- 3.4 Results
- 4 Conclusion
- References
- Machines Learning for Mixed Reality
- 1 Introduction
- 2 Milan Cathedral Survey. A Brief Overview
- 3 Multi-level Multi-resolution Classification
- 3.1 Methodology
- 3.2 Classification
- 4 Mixed Reality System
- 4.1 MR to Support Maintenance Works in Complex Architecture
- 4.2 The Developed Prototype
- 5 Conclusion and Future Works
- References
- From Fully Supervised to Blind Digital Anastylosis on DAFNE Dataset
- 1 Introduction
- 2 Related Works
- 3 The DAFNE Challenge
- 3.1 The Supervised Approach
- 4 Blind Digital Anastylosis
- 4.1 Blind Digital Anastylosis Like a Hard Jigsaw Puzzle Problem
- 4.2 A Preliminary Approach
- 5 Experimental Results
- 6 Conclusions
- References
- Restoration and Enhancement of Historical Stereo Photos Through Optical Flow
- 1 Introduction
- 2 Method Description
- 2.1 Auxiliary Image Point-Wise Transfer
- 2.2 Color Correction
- 2.3 Data Fusion
- 2.4 Refinement
- 3 Evaluation
- 3.1 Dataset
- 3.2 Compared Methods
- 3.3 Results
- 4 Conclusion and Future Work
- References
- Automatic Chain Line Segmentation in Historical Prints
- 1 Introduction
- 2 Related Work
- 3 Method
- 3.1 Line Segmentation Network
- 3.2 Line Detection and Parameterization
- 4 Evaluation
- 4.1 Dataset
- 4.2 Implementation Details and Evaluation Metrics
- 4.3 Results
- 5 Conclusion
- References
- Documenting the State of Preservation of Historical Stone Sculptures in Three Dimensions with Digital Tools
- 1 Introduction
- 2 Overview and Methods
- 3 Application and Results
- 4 Discussion and Conclusions
- References
- FBE2020 - Workshop on Facial and Body Expressions, micro-expressions and behavior recognition
- Workshop on Facial and Body Expressions, micro-expressions and behavior recognition (FBE2020)
- Organization
- FBE2020 Workshop Chairs
- Website Chair
- Program Committee
- Additional Reviewer
- FBE 2020 Organizers
- Motion Attention Deep Transfer Network for Cross-database Micro-expression Recognition
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 3.1 Motion Attention Representation
- 3.2 Deep Transfer Network
- 4 Experiments
- 4.1 Databases
- 4.2 Implementation Details
- 4.3 Results
- 4.4 Ablation Analysis
- 5 Conclusion
- References
- Spatial Temporal Transformer Network for Skeleton-Based Action Recognition
- 1 Introduction
- 2 Spatial Temporal Transformer Network
- 2.1 Spatial Self-Attention (SSA)
- 2.2 Temporal Self-Attention (TSA)
- 2.3 Two-Stream Spatial Temporal Transformer Network
- 3 Model Evaluation
- 3.1 Datasets
- 3.2 Experimental Settings
- 3.3 Results
- 4 Comparison with State-of-the-Art
- 5 Conclusions
- References
- Slow Feature Subspace for Action Recognition
- 1 Introduction
- 2 Related Work
- 2.1 Slow Feature Analysis for Action Recognition
- 2.2 Subspace-Based Methods for Action Recognition
- 3 Proposed Method and Framework
- 3.1 Subspace Representation of Slowly Varying Components
- 3.2 Proposed Framework for Action Recognition
- 4 Experimental Results and Discussions
- 4.1 Experiment with KTH Action Dataset
- 4.2 Experiments with Isolated SLR500 Dataset
- 5 Conclusion and Future Work
- References
- Classification Mechanism of Convolutional Neural Network for Facial Expression Recognition
- 1 Introduction
- 2 Manifolds in Deep Learning
- 2.1 Encoder and Decoder
- 2.2 Traditional Expression Recognition Method
- 3 Proposed Model
- 3.1 Model Design
- 4 Classification Mechanism Analysis of the Network
- 4.1 Deconvolution Visualization
- 4.2 Facial Action Units
- 5 Experiments and Results
- 5.1 Dataset and Implementation
- 5.2 Criteria for Distance Measurement
- 5.3 Results
- 6 Conclusions
- References
- Applying Delaunay Triangulation Augmentation for Deep Learning Facial Expression Generation and Recognition
- 1 Introduction
- 2 Dataset
- 3 Augmented Dataset
- 3.1 Alignment, Centering and Cropping
- 3.2 Computing Delaunay Triangulation and Transform
- 3.3 Limitations
- 4 Facial Expression Generation Task
- 5 Facial Expression Recognition Task
- 6 Discussion
- 7 Future Work
- 8 Conclusion
- References
- Deformable Convolutional LSTM for Human Body Emotion Recognition
- 1 Introduction
- 2 Method
- 2.1 Input Preprocessing
- 2.2 Network Architecture
- 3 Experiments
- 3.1 Dataset
- 3.2 Comparison Between Deformable ConvLSTM and ConvLSTM
- 3.3 Comparison Between Other Methods
- 4 Conclusion
- References
- Nonlinear Temporal Correlation Based Network for Action Recognition
- 1 Introduction
- 2 Related Work
- 3 Proposed Network
- 3.1 Spatial-Temporal Separable Convolution
- 3.2 Correlation Based Feature Learning
- 3.3 NTE Block Design
- 3.4 Nonlinear Temporal Networks
- 4 Experiments
- 4.1 Datasets
- 4.2 Experimental Setup
- 4.3 Ablation Study
- 4.4 Study on the Change of Accuracy
- 4.5 Comparison with State-of-art on Mini-Kinetics-200 Datasets
- 4.6 Comparison with State-of-art on UCF-101 and HMDB-51
- 5 Conclusion
- References
- Author Index
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