Pattern Recognition

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

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

Springer (Publisher)

Published on 4. December 2024

XXXVII, 434 pages

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978-3-031-78389-0 (ISBN)

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Content

Intro
President's Address
Preface
Organization
Contents - Part XXV
A Novel Loss for Contrastive Deep Supervision
1 Introduction
2 Related Work
2.1 Contrastive Learning
2.2 Supervised Contrastive Learning
2.3 Deep Supervision
3 Method
3.1 NCDS Framework
3.2 Analysis of .
3.3 The Novel Loss
4 Experiment
5 Conclusion
References
Multi-Task Interaction Network Based on a Cross-Attention Fusion Mechanism for Offline Signature Verification
1 Introduction
2 Related Work
3 Method
3.1 Network Architecture
3.2 Contrastive Interaction Module
3.3 Self-Channel Interaction Module
4 Experiments
4.1 Ablation Studies
4.2 Comparison with State of the Art
4.3 Cross-Language Test
4.4 Visualization
5 Conclusion
References
Functional Tensor Decompositions for Physics-Informed Neural Networks
1 Introduction
2 Theoretical Background
2.1 Universal Approximation Theorem
2.2 Physics-Informed Neural Networks
2.3 Functional Tensor Decompositions for PINNs
3 Experiments
4 Discussion and Conclusions
References
Squeeze and Hypercomplex Networks on Leaf Disease Detection
1 Introduction
2 Literature Reviews
2.1 Rice Leaf Diseases Detection
2.2 Wheat Leaf Diseases Detection
2.3 Corn Leaf Diseases Detection
2.4 New Plant Leaf Diseases Data
3 Background Works
3.1 Residual 1D Convolutional Networks
3.2 Quaternion Convolution Networks
3.3 Parameterized Hypercomplex Multiplication Layer
3.4 Squeeze-and-Excitation Network
4 Proposed Squeeze-and-Hypercomplex Network
5 EXPERIMENTAL RESULTS
5.1 Dataset Description
5.2 Method
5.3 Result Analysis
5.4 Comparison with the Literature
5.5 Ablation Study
6 Conclusion
References
Mangoes Ripeness Grading: Vision Based Approach
1 Introduction
2 Materials and Methods
2.1 Data Sets and Experimentation Setup
2.2 Data Augmentation
2.3 Proposed Methodology
3 Results and Discussion
4 Conclusion
References
FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learning
1 Introduction
2 Related Work
3 Method
3.1 The Rewind Strategy
4 Results
4.1 Federated Learning Performance
4.2 Continual Federated Learning
5 Conclusion
References
On the Relationship Between Double Descent of CNNs and Shape/Texture Bias Under Learning Process
1 Introduction
2 Related Work
2.1 Double descent
2.2 CNN for image understanding
3 Correlation analysis framework of double descent and shape/texture bias
3.1 How to observe double descent
3.2 Phases of learning curve with double descent
3.3 Quantifying the shape/texture bias of the model
4 Experiments
4.1 Nakkiran's setting
4.2 Ablation studies and analyses
4.3 Layer-wise analyses and visualization
5 Discussion
6 Conclusion
References
Cystic Adenocarcinoma Segmentation Based on Multi-frequency and Multi-scale SimAM Attention
1 Introduction
2 Related Works
2.1 Model Architecture
2.2 Attention Mechanisms
3 Methods
3.1 Model Architecture
3.2 Fusion of Shallow Features And Deep Semantic Features Unit
3.3 Multi-Frequency in Multi-Scale SimAM Attention
4 Experiments
4.1 Datasets
4.2 Evaluation Metrics
4.3 Experiment Settings
4.4 Comparison With SOTA Models
4.5 Ablation Study On LungSSFNet
5 Conclusion
References
MSDNet: A Multi-scale Dense Network for Chip Surface Defect Segmentation
1 Introduction
2 Related Works
2.1 Defect Classification
2.2 Defect Detection
2.3 Defect Segmentation
3 Method
3.1 Architecture
3.2 Multi-scale Convolution Module
3.3 Node Module
3.4 Attention Module
3.5 Loss Function
4 Experiments
4.1 Dataset
4.2 Implementation Details
4.3 Experimental Results
4.4 Ablation Study
5 Conclusion
References
Task Oriented Image Quality Assessment for Synthesized Images
1 Introduction
2 Related Work
2.1 Reference-guided image synthesis (RIS)
2.2 Image Quality Assessment(IQA)
3 Methodology
3.1 Style Level Interpolation for Data Preparation
3.2 Learning-based Quality Score Estimation
3.3 Training Objective
4 EXPERIMENT
4.1 Dataset
4.2 Protocol and Evalution criteria
4.3 Performance Evalution
4.4 CONCLUSION
References
SANGAM: Synergizing Local and Global Analysis for Simultaneous WBC Classification and Segmentation
1 Introduction
2 Related Work
2.1 WBC Segmentation
2.2 WBC Classification
3 Proposed System
3.1 WBC Segmentation
3.2 WBC Classification
3.3 Refined WBC Segmentation
4 Experimental Results
4.1 Experimental settings
4.2 Implementation details
4.3 Training and Testing settings
4.4 Comparative WBC Segmentation Performance
4.5 Comparative WBC Classification Performance
4.6 Ablation study
5 Conclusion
References
MeDiANet: A Lightweight Network for Large-scale Multi-disease Classification of Multi-modal Medical Images Using Dilated Convolution and Attention Network
1 Introduction
2 Related Work
3 Methodology
3.1 Residual Block
3.2 Multi Dilated residual block
3.3 Dilated Residual Attention Block
3.4 MeDiANet
4 Experimental Setup
4.1 Dataset
4.2 Implementation Details
4.3 Evaluation Metrics
5 Results & Discussions
6 Conclusion
References
A Data Augmentation Approach for Well Log Interpretation
1 Introduction
2 Related Works
2.1 Data Augmentation
2.2 Time-frequency Augmentation
3 Method
3.1 Time-domain Method
3.2 Frequency-domain Method
4 Experiment
4.1 Dataset
4.2 Experimental Setting
4.3 Experimental Results
5 Conclusion
References
TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines
1 Introduction
2 Related Work
3 Apparatus and Dataset
4 Knowledge Distillation Approach
5 Result and Discussion
6 Conclusion
References
ArtNeRF: A Stylized Neural Field for 3D-Aware Artistic Face Synthesis
1 Introduction
2 Related works
2.1 Style Transfer with 2D GAN
2.2 3D-aware Image Synthesis
3 Method
3.1 Preliminaries
3.2 Self-supervised Style Encoder
3.3 Conditional Generative Radiance Field
3.4 Neural Rendering Module
3.5 Triple Discriminator Network
3.6 Loss Functions
4 Experiments
4.1 Comparisons
4.2 Ablation Study
5 Conclusion
References
Latent Behavior Diffusion for Sequential Reaction Generation in Dyadic Setting
1 Introduction
2 Related Works
2.1 Deterministic reaction synthesis
2.2 Multiple Reaction Generation
3 Proposed Method
3.1 Problem definition
3.2 Facial Reaction Compression
3.3 Latent Behavior Diffusion
4 Experiments
4.1 Evaluation setup
4.2 Evaluation metric
4.3 Results
4.4 Ablation Study
5 Conclusion
References
CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks
1 Introduction
2 Literature Review
3 Method
3.1 Continual Few-Shot GAN
3.2 Cross Domain Consistency Loss
3.3 Teacher Student Model
4 Experiments
4.1 Qualitative Results
4.2 Quantitative Results
4.3 Ablation Study
5 Conclusion
References
T2R-GAN: A CGAN-based model for rural thematic road extraction
1 Introduction
2 Related Work
3 Methodology
3.1 Overview of T2R-GAN
3.2 ELAU-Net Generator and PatchGAN Discriminator
3.3 Bilateral Hinge Loss
4 Experiment
4.1 Experimental Setting
5 Results
6 Conclusion
References
d-Sketch: Improving Visual Fidelity of Sketch-to-Image Translation with Pretrained Latent Diffusion Models without Retraining
1 Introduction
2 Related Work
3 Method
3.1 Preliminaries
3.2 Latent Code Translation Network (LCTN)
4 Experiments
5 Conclusions
References
Semantically Consistent Person Image Generation
1 Introduction
2 Related Work
3 Method
3.1 Coarse Generation Network
3.2 Data-Driven Refinement Strategy
3.3 Appearance Attribute Transfer and Rendering
4 Experimental Setup
5 Results
6 Ablation Study
7 Limitations
8 Conclusions
References
GM-GAN: Geometric Generative Models Based on Morphological Equivariant PDEs and GANs
1 Introduction
2 Equivariance and homogeneous spaces on Riemannian manifolds
3 Group morphological convolutions and PDEs
4 Morphological equivariant PDEs for generative models
4.1 Morphological PDE-based layers
4.2 PDE model design
4.3 Architecture of morphological equivariant PDEs based on GAN
5 Numerical experiments
6 Conclusion and perspectives
References
NR-CION: Non-rigid Consistent Image Composition Via Diffusion Model
1 Introduction
2 Related work
2.1 Text-based image editing
2.2 Image composition
2.3 Image inversion
3 Preliminary
3.1 Latent diffusion model
3.2 Classifier free guidance
3.3 Attention mechanism
4 Method
4.1 Image inversion
4.2 Non-rigid foreground object generation and mask generation
4.3 Image composition
5 Experiments
5.1 Implementation details and benchmark
5.2 Compared with previous methods
5.3 Ablation study
6 Limitation and future work
7 Summary
References
Neighborhood Feature Enhancement Flow Diffusion Model for Point Cloud Generation
1 Introduction
2 Related Work
2.1 GAN and VAE-based methods
2.2 Flow and Autoregression-based methods
2.3 Diffusion based methods
3 Methods
3.1 Multi-scale Neighborhood Feature Aggregation Module
3.2 Neighborhood Attention-based Feature Enhancement Module
3.3 Feature-to-Flow Data Transformation Module
3.4 Generation process
4 Experiments
4.1 Experiment Settings
4.2 Comparison to State-of-the-art Works
4.3 Ablation Study
5 Conclusion
References
Beta-Sigma VAE: Separating Beta and Decoder Variance in Gaussian Variational Autoencoder
1 Introduction
2 Background
3 Beta-Sigma VAE
3.1 Categorizing Blurriness
3.2 Problem Investigation
3.3 Method
4 Experimental Evaluation
4.1 Evaluation Setup
4.2 Experimental Results
5 Conclusion
References
HingeRLC-GAN: Combatting Mode Collapse with Hinge Loss and RLC Regularization
1 Introduction
2 Related Work
3 Proposed Method
3.1 Architectural Overview
3.2 Loss Functions
3.3 Regularization
3.4 Mathematical Intuition for Improved Mode Coverage
4 Experimental Analysis
4.1 Comparison of GAN Architectures
4.2 Comparison of Loss Functions
4.3 Comparison of Regularization Methods
4.4 Comparison of GAN Models
4.5 Mode Capture Analysis
4.6 Evaluation of HingeRLC-GAN
4.7 Generated Images
5 Conclusion
References
LDFaceNet: Latent Diffusion-Based Network for High-Fidelity Deepfake Generation
1 Introduction
2 Related Work
2.1 Models for Image Synthesis
2.2 Face Swapping Models
3 Preliminary: Diffusion Models
4 Methodology
4.1 Source Identity Guided Diffusion
4.2 Target Segmentation Guided Diffusion
4.3 Background Preservation
5 Results and Discussion
5.1 Quantitative and Qualitative results
5.2 Ablation Study
6 Conclusion
References
Adaptive Graph Convolutional Fusion Network for Skeleton-Based Abnormal Gait Recognition
1 Introduction
2 Related work
2.1 Skeleton-based abnormal gait recognition
2.2 Graph fusion network
3 Methods
3.1 Pipeline overview
3.2 Spatiotemporal gait graph construction
3.3 Adaptive graph convolutional fusion layer
4 Experiments
4.1 Dataset
4.2 Training setting and evaluation metrics
4.3 Evaluation of the generalization performance of our proposed method
4.4 Evaluation of the complexity of our proposed method
4.5 Ablation experiments
5 Conclusion
References
ConDGAD: Multi-augmentation Contrastive Learning for Dynamic Graph Anomaly Detection
1 Introduction
2 Related Work
3 Methodology
3.1 Creating Dynamic Graphs from Time Series
3.2 ConDGAD Process
3.3 Anomaly Detection Process
4 Experiments
4.1 Experiments Design
4.2 Evaluation Matrix
4.3 Experiment Results
5 Conclusion
References
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Pattern Recognition

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