Pattern Recognition

Name: Pattern Recognition | 27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part IX
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27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part IX

Apostolos Antonacopoulos Subhasis Chaudhuri Rama Chellappa Cheng-Lin Liu Saumik Bhattacharya Umapada Pal(Editor)

Springer (Publisher)

Published on 10. December 2024

XXXVII, 472 pages

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978-3-031-78189-6 (ISBN)

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Content

Intro
President's Address
Preface
Organization
Contents - Part IX
Mask and Compress: Efficient Skeleton-Based Action Recognition in Continual Learning
1 Introduction
2 Related Works
3 Method
3.1 Preliminaries
3.2 CHARON
4 Experimental Analysis
4.1 Datasets
4.2 Implementation Details
4.3 Results
4.4 Ablations
5 Conclusions
References
Text-Driven Prototype Learning for Few-Shot Class-Incremental Learning
1 Introduction
2 Related Works
3 Method
4 CIFAR100-Text and MiniImageNet-Text Datasets
5 Experiments
5.1 Datasets and Settings
5.2 Text-Driven Prototype Analysis
6 Discussions
7 Conclusion
References
Dual Supervised Contrastive Learning Based on Perturbation Uncertainty for Online Class Incremental Learning
1 Introduction
2 Related Work
2.1 Continual Learning
2.2 Contrastive Learning
3 Method
3.1 Problem Definition
3.2 Method Overview
3.3 Perturbation Uncertainty Based Memory Retrieval
3.4 Supervised Contrastive Learning
4 Experiment
4.1 Experiment Setup
4.2 Performance Comparison
4.3 Ablation Study
5 Conclusion
References
Breaking Information Silos: Global Guided Task Prediction for Class-Incremental Learning
1 Introduction
2 Related Work
3 Methods
3.1 Problem Setting and Method Overview
3.2 Local De-Redundant Module
3.3 Global Information Module
3.4 Attention Module
3.5 Optimizing and Lightweight Model
4 Experiments
4.1 Experiment Setup and Implementation Details
4.2 Results and Discussion
4.3 Ablation Study and Analysis
5 Conclusion
References
Conditioned Prompt-Optimization for Continual Deepfake Detection
1 Introduction
2 Related Work
3 Preliminaries
3.1 Problem Formulation
3.2 Prompt Tuning
4 Prompt2Guard
4.1 Text-Prompt Conditioning
4.2 Continual Read-Only Prompts
4.3 Predictions Ensembling
5 Experiments
5.1 Comparative Results
5.2 Ablations
6 Conclusions
References
Plasticity Driven Knowledge Transfer for Continual Deep Reinforcement Learning in Financial Trading
1 Introduction
2 Related Work
3 Proposed Method
3.1 Continual Learning Approach
3.2 Temporal Focused Sampling Experience Replay
3.3 Knowledge Transfer Methodology
4 Experimental Evaluation
4.1 Dataset and Feature Extraction
4.2 Model Architecture
4.3 Results
5 Conclusions
References
Orthogonal Latent Compression for Streaming Anomaly Detection in Industrial Vision
1 Introduction
2 Related Work
3 Method
3.1 Overview
3.2 Orthogonal Latent Compression
3.3 Loss Function
3.4 Abnormal Scores
4 Experiments
4.1 Experimental Setup
4.2 Evaluation on Streaming AD
4.3 Evaluation on Offline AD
4.4 Ablation Analysis
5 Conclusion
References
Out-of-Distribution Forgetting: Vulnerability of Continual Learning to Intra-class Distribution Shift
1 Introduction
2 Related Works
2.1 Continual Learning
2.2 Security Concerns of Neural Networks
2.3 Several Concerns of Continual Learning
3 Out-of-Distribution Forgetting
3.1 Standard CL Paradigm
3.2 OODF Paradigm
3.3 Introducing of the Distribution Shift
4 Experiment Settings
5 Properties of OODF
5.1 Delayed Effect
5.2 Targeting
5.3 Continual Detrimental
6 Analysis
6.1 Occlusion Strength
6.2 Various Conditions of Shift
6.3 Shift Position in the Learning Sequence
6.4 Different Percentage r and Strength
6.5 Mechanism of OODF
6.6 Proposal for Improving OODF
7 Conclusion
References
Generating Multi-objective Fronts from Streamed Data Using Nested List
1 Introduction
1.1 Prerequisites
1.2 Literature Survey
1.3 Gap Identification and Motivation
1.4 Salient Points of the Proposed Approach
1.5 Organization of the Paper
2 A Nested List Structure for Non-dominated Sorting of Streamed Data Elements
2.1 Benchmark Data-Set Specification
3 Result and Discussion
3.1 Complexity Analysis
3.2 Correctness and Completeness
4 Conclusion
References
Mapping the Unknown: A New Approach to Open-World Video Recognition
1 Introduction
2 Related Work
3 Dynamic Ensembles for OWR
3.1 Ensemble Decision Module
3.2 Generation Ensemble Module
3.3 Update and Limit Module
4 Experiments Preliminary
4.1 Experiment Dataset Configuration
4.2 Experimental Setup
5 Experiment Results
5.1 Comparison Against State-of-the-Art Face Recognition in OWR
5.2 Sensitivity About Parameters
6 Conclusions
References
ESL: Explain to Improve Streaming Learning for Transformers
1 Introduction
2 Related Work
3 Proposed Framework: ESL
3.1 XAI Method: Rollout Feature Explanation Method (RFEM)
3.2 Input Patch Selection
3.3 Streaming Learner: Entropy-Based Move-To-Data (EMTD) and Retargeting (EMTDR)
4 Experiments and Results
4.1 Experimental Details
4.2 Results
4.3 Ablation Study
5 Conclusion and Future Work
References
Detection of Unknown Errors in Human-Centered Systems
1 Introduction
1.1 Contributions
1.2 Paper Organization
2 Preliminaries
2.1 Signal Temporal Logic
2.2 Physics-Driven Surrogate Model
3 Coefficient Mining from Trajectory
3.1 Dynamics Induced RNN
3.2 Forward Pass in DiH-RNN
3.3 Backpropagation to Learn Coefficients
4 Conformal Inference
5 Case Studies
5.1 Automated Insulin Delivery System Example
5.2 Aircraft Example
5.3 Autonomous Driving Example
6 Evaluation Method and Metrics
6.1 Unknown-Unknown Scenario Simulation
6.2 Baseline Strategy
7 Results
7.1 Automated Insulin Delivery System Example
7.2 Aircraft Example
7.3 Autonomous Driving Example
8 Future Works
9 Conclusions
References
Source-Free Test-Time Adaptation For Online Surface-Defect Detection
1 Introduction
2 Related Work
3 Method
3.1 Supervisor
3.2 Augmented Mean Prediction
3.3 Dynamically-Balancing Loss
3.4 Model Update Pipeline
4 Experiments
4.1 Datasets and Pre-training
4.2 Results
4.3 Ablation Study
5 Conclusion
References
Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models
1 Introduction
2 Related Works
3 Proposed Method
3.1 Emotion-Centered Generative Replay
3.2 General Pipeline
4 Experiments
4.1 Results
5 Discussion
6 Conclusion
A Appendix
A.1 Evaluation of the MNIST Dataset
References
Satellite State Prediction and Maneuver Detection Analysis Using NCDEs
1 Introduction
2 Related Work
3 Proposed Method
3.1 Dataset
3.2 Data Preprocessing Step
3.3 SSPMDA Architecture
4 Empirical Evaluations
4.1 Experimental Results
4.2 Ablation Study
5 Visualization
6 Conclusion
References
MIXAD: Memory-Induced Explainable Time Series Anomaly Detection
1 Introduction
2 Related Works
3 Method
3.1 Problem Formulation
3.2 Spatiotemporal Recurrent Convolution Unit
3.3 Memory-Augmented Graph Structure Learning
3.4 Memory-Induced Explainable Anomaly Detection (MIXAD)
3.5 Anomaly Scoring
3.6 Anomaly Interpretation
4 Experiments and Analysis
4.1 Datasets and Baselines
4.2 Evaluation Metrics
4.3 Performance Comparisons
4.4 Ablation Study
4.5 Visualization of Node Embeddings
4.6 Visualization of Anomaly Scores
5 Case Study: Exathlon Dataset and Testbed
6 Conclusion
References
Rough Set Theoretic Approach for Solving the Multi-Armed Bandit Problems
1 Introduction
2 Applying Rough Set Concepts to Stochastic Multi-Armed Bandits
3 Proposed Methodology
4 Experimental Results
4.1 Bandit Problem
4.2 Advertising Problem
4.3 Election Campaign Problem
4.4 Ablation Study
4.5 Discussions
5 Conclusions and Future Work
References
Hybrid Graph Representation Learning: Integrating Euclidean and Hyperbolic Space
1 Introduction
2 Related Work
2.1 Representation Learning
2.2 Hyperbolic Representation Learning
3 Preliminaries
3.1 Poincaré Ball Model
3.2 Gyrovector Spaces
4 Method
4.1 Hyperbolic Encoder
4.2 Euclidean Loss
4.3 Hyperbolic Hierarchy Loss
4.4 Hyperbolic Uniformity Loss
4.5 Total Loss
5 Experiments
5.1 Experimental Setup
5.2 Node Classification and Clustering
5.3 Visualization
5.4 Ablation Study
6 Conclusion
References
Learning Object Focused Attention
1 Introduction
2 Object Focused Attention
3 Self-supervised Option with MAE
4 Adjacency Regularization
5 Related Work
5.1 Transformers and Self-Attention
5.2 Holistic Shape Representation
5.3 Multi-label Classification
6 Experimental Evaluation
6.1 Multi-label Classification on MS-COCO and Pascal Voc2012
6.2 Out-of-Distribution Background Corruption with Stable Diffusion
6.3 Learning Shape Representations over Textures
7 Discussion and Future Work
References
Stereographic Projection for Embedding Hierarchical Structures in Hyperbolic Space
1 Introduction
2 Background
2.1 Hyperbolic Neural Networks
2.2 Topic Model
3 Stereographic Projection Transition Mapping
3.1 Limitations of Exponential Mapping for Hierarchical Embeddings
3.2 Method: Stereographic Projection Transition Mapping
3.3 Optimization Algorithm for SPTM
4 Experiment
5 Conclusion
References
SPCSE: Soft Positive Enhanced Contrastive Learning for Sentence Embeddings
1 Introduction
2 Related Works
2.1 Contrastive Learning in Sentence Representation
2.2 Positive and Negative Instances
2.3 Alignment and Uniformity in Contrastive Learning
3 Approach
3.1 Unsupervised Contrastive Learning
3.2 Contrastive Learning with Soft Positive
4 Experiments
4.1 Evaluation Tasks
4.2 Training Details
4.3 Main Results
4.4 Ablation Study
5 Analysis
5.1 Alignment-Uniformity Analysis
5.2 Hyper-parameters Analysis
5.3 Distribution of Sentence Embedding
6 Conclusion
A Appendix A
A.1 Training Detail
A.2 Discrete Data Argumentation Methods
References
Neural Topic Model with Distance Awareness
1 Introduction
2 Background
2.1 Neural Topic Models and Optimal Transport
2.2 Manifold Learning
3 Proposed Model
4 Related Work
5 Experiments
5.1 Experimental Settings
5.2 Quantitative Results
5.3 Visualization Analysis
6 Conclusion
References
Ontology-Guided Deep Metric Learning and Applications to Obstetrics
1 Related Works
1.1 Guiding DML with Natural Language Inputs
1.2 Leveraging Hierarchical Annotations to Guide the Learning
2 Methodology
2.1 Leveraging Structured Annotations for Image Similarity
2.2 Integrating Language Information
3 Experiments
3.1 Guiding the Metric Learning with Prior Meta Annotations
3.2 Integrating Structured Annotation Through Natural Language
4 Conclusion
References
CoFE: Consistency-Driven Feature Elimination for eXplainable AI
1 Introduction
2 Background
2.1 Limitations of Current Approaches
2.2 Our Contributions
3 Proposed Feature Selection Approach
3.1 Coefficient Sign Stability (CoSS)
4 Experimental Setup
5 Results and Discussion
5.1 CoSS Gain
5.2 RMSE Loss
5.3 Jensen-Shannon Distance of CoSS and RMSE Scores
5.4 Discussion
6 Visualizing CoFE's Impact on Explainability
7 Conclusion
References
From One to Many Lorikeets: Discovering Image Analogies in the CLIP Space
1 Introduction
2 Related Work
3 Method
3.1 Image Encoding
3.2 Analogy Discovery
4 Experiments
4.1 Experimental Details
4.2 Discussion
4.3 Similarity of Discovered Analogies
4.4 Effect of Number of Clusters
5 Limitations and Future Work
6 Conclusion
References
A Framework for Mining Collectively-Behaving Bots in MMORPGs
1 Introduction
2 Background
2.1 Trajectory Data Mining for Real World Tasks
2.2 Bot Detection and Trajectory Mining for MMORPGs
3 Proposed Approach
3.1 Preliminary
3.2 Data Preparation
3.3 Task-Specific Representation Model
3.4 Extract Representation Vectors
3.5 Clustering Collectively-Behaving Groups
4 Experiments
4.1 Dataset : Lineage W
4.2 Evaluation Methods
4.3 Ablation Study
4.4 Baseline Models
4.5 Trajectory Visualization
5 Conclusion
References
Causal Deep Learning
1 Introduction
1.1 Causal Inference Versus Regression
1.2 Causal Neural Networks
2 Forward Causal Question: ``What If?''
2.1 Training Data
2.2 Tensor Factor Analysis Model
2.3 Kernel Tensor Factor Analysis Model
2.4 Neural Network Architecture
2.5 Causal Deep Networks and Scalable Tensor Factor Analysis:
3 Inverse Causal Question: ``Why?''
4 Conclusion
References
Non-symmetrical Confidence Interval of AUC Measure Based on Cross-Validation
1 Introduction
2 Related Work
3 AUC Measures Based on K-Fold Cross-Validation
3.1 ROC Curve and AUC Measure
3.2 Micro-averaged AUC Measure Based on K-Fold Cross-Validation
3.3 Macro-averaged AUC Measure Based on K-Fold Cross-Validation
4 Confidence Interval of AUC Measure Based on K-Fold Cross-Validated Beta Distribution
4.1 Confidence Interval of AUC Measure Based on K-Fold Cross-Validated t-Distribution
4.2 Confidence Interval of AUC Measure Based on Corrected K-Fold Cross-Validated t-Distribution
4.3 Confidence Interval of AUC Measure Based on K-Fold Cross-Validated Beta Distribution
5 Experimental Results and Analysis
5.1 Experimental Settings
5.2 Experimental Results and Analysis of Simulated Data
5.3 Experimental Results and Analysis of Real Data
References
Visualizing and Generalizing Integrated Attributions
1 Introduction
2 Related Work
3 Generalized Integrated Attributions
3.1 Visualization of Pixel Attributions
3.2 Extending Expected Gradients
3.3 Novel Attribution Measures
4 Evaluation Using Quantus ch29hedstrom2023quantus
5 Conclusion
5.1 Limitations
References
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Pattern Recognition

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