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[null,null,"Autore",null,"Antonacopoulos Apostolos"]
[null,null,"Titolo",null,"Pattern Recognition : 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part XXV / / edited by Apostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"9783031783890"]
[{"$nulls":4},"3031783891"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (470 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 15325"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Computer vision"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Machine Learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Diseases Detection -- 2.4 New Plant Leaf Diseases Data -- 3 Background Works -- 3.1 Residual 1D Convolutional Networks -- 3.2 Quaternion Convolution Networks -- 3.3 Parameterized Hypercomplex Multiplication Layer -- 3.4 Squeeze-and-Excitation Network -- 4 Proposed Squeeze-and-Hypercomplex Network -- 5 EXPERIMENTAL RESULTS -- 5.1 Dataset Description -- 5.2 Method -- 5.3 Result Analysis -- 5.4 Comparison with the Literature -- 5.5 Ablation Study -- 6 Conclusion -- References -- Mangoes Ripeness Grading: Vision Based Approach -- 1 Introduction."]
[{"$nulls":4},"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."]
[{"$nulls":4},"3.5 Loss Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Task Oriented Image Quality Assessment for Synthesized Images -- 1 Introduction -- 2 Related Work -- 2.1 Reference-guided image synthesis (RIS) -- 2.2 Image Quality Assessment(IQA) -- 3 Methodology -- 3.1 Style Level Interpolation for Data Preparation -- 3.2 Learning-based Quality Score Estimation -- 3.3 Training Objective -- 4 EXPERIMENT -- 4.1 Dataset -- 4.2 Protocol and Evalution criteria -- 4.3 Performance Evalution -- 4.4 CONCLUSION -- References -- SANGAM: Synergizing Local and Global Analysis for Simultaneous WBC Classification and Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 WBC Segmentation -- 2.2 WBC Classification -- 3 Proposed System -- 3.1 WBC Segmentation -- 3.2 WBC Classification -- 3.3 Refined WBC Segmentation -- 4 Experimental Results -- 4.1 Experimental settings -- 4.2 Implementation details -- 4.3 Training and Testing settings -- 4.4 Comparative WBC Segmentation Performance -- 4.5 Comparative WBC Classification Performance -- 4.6 Ablation study -- 5 Conclusion -- References -- MeDiANet: A Lightweight Network for Large-scale Multi-disease Classification of Multi-modal Medical Images Using Dilated "]
[{"$nulls":4},"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 &amp -- 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."]
[{"$nulls":4},"3.2 Frequency-domain Method -- 4 Experiment -- 4.1 Dataset -- 4.2 Experimental Setting -- 4.3 Experimental Results -- 5 Conclusion -- References -- TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines -- 1 Introduction -- 2 Related Work -- 3 Apparatus and Dataset -- 4 Knowledge Distillation Approach -- 5 Result and Discussion -- 6 Conclusion -- References -- ArtNeRF: A Stylized Neural Field for 3D-Aware Artistic Face Synthesis -- 1 Introduction -- 2 Related works -- 2.1 Style Transfer with 2D GAN -- 2.2 3D-aware Image Synthesis -- 3 Method -- 3.1 Preliminaries -- 3.2 Self-supervised Style Encoder -- 3.3 Conditional Generative Radiance Field -- 3.4 Neural Rendering Module -- 3.5 Triple Discriminator Network -- 3.6 Loss Functions -- 4 Experiments -- 4.1 Comparisons -- 4.2 Ablation Study -- 5 Conclusion -- References -- Latent Behavior Diffusion for Sequential Reaction Generation in Dyadic Setting -- 1 Introduction -- 2 Related Works -- 2.1 Deterministic reaction synthesis -- 2.2 Multiple Reaction Generation -- 3 Proposed Method -- 3.1 Problem definition -- 3.2 Facial Reaction Compression -- 3.3 Latent Behavior Diffusion -- 4 Experiments -- 4.1  Evaluation setup -- 4.2 Evaluation metric -- 4.3 Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks -- 1 Introduction -- 2 Literature Review -- 3 Method -- 3.1 Continual Few-Shot GAN -- 3.2 Cross Domain Consistency Loss -- 3.3 Teacher Student Model -- 4 Experiments -- 4.1 Qualitative Results -- 4.2 Quantitative Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- T2R-GAN: A CGAN-based model for rural thematic road extraction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview of T2R-GAN."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"editing -- 2.2 Image composition -- 2.3 Image inversion -- 3 Preliminary -- 3.1 Latent diffusion model -- 3.2 Classifier free guidance -- 3.3 Attention mechanism -- 4 Method -- 4.1 Image inversion -- 4.2 Non-rigid foreground object generation and mask generation -- 4.3 Image composition -- 5 Experiments -- 5.1 Implementation details and benchmark -- 5.2 Compared with previous methods -- 5.3 Ablation study -- 6 Limitation and future work -- 7 Summary -- References."]
[{"$nulls":4},"Neighborhood Feature Enhancement Flow Diffusion Model for Point Cloud Generation."]
[null,null,null,"Sommario/riassunto","The multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1–5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics."]