27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part I Herausgegeben:Antonacopoulos, Apostolos; Chaudhuri, Subhasis; Chellappa, Rama; Liu, Cheng-Lin; Bhattacharya, Saumik; Pal, Umapada
27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part I Herausgegeben:Antonacopoulos, Apostolos; Chaudhuri, Subhasis; Chellappa, Rama; Liu, Cheng-Lin; Bhattacharya, Saumik; Pal, Umapada
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…mehr
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.
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling.- Deep Evidential Active Learning with Uncertainty-Aware Determinantal Point Process.- Knowledge Distillation in Deep Networks under a Constrained Query Budget.- Adabot: An Adaptive Trading Bot using an Ensemble of Phase-specific Few-shot Learners to Adapt to the Changing Market Dynamics.- Uncertainty in Ambiguity of Data.- When Uncertainty-based Active Learning May Fail.- Customizable and Programmable Deep Learning.- SegXAL: Explainable Active Learning for semantic segmentation in driving scene scenarios.- AMC-OA: Adaptive Multi-Scale Convolutional Networks with Optimized Attention for Temporal Action Localization.- Comparative Analysis Of Pretrained Models for Text Classification, Generation and Summarization : A Detailed Analysis.- Predicting Judgement Outcomes from Legal Case File Summaries with Explainable Approach.- Multi-view Ensemble Clustering-based Podcast Recommendation in Indian Regional Setting.- Privacy-Preserving Ensemble Learning using Fully Homomorphic Encryption.- Capturing Temporal Components for Time Series Classification.- Hierarchical Transfer Multi-task Learning Approach for Scene Classification.- Deep Prompt Multi-task Network for Abuse Language Detection.- All mistakes are not equal: Comprehensive Hierarchy Aware Multilabel Predictions (CHAMP).- IDAL: Improved Domain Adaptive Learning for Natural Images Dataset.- Large Multimodal Models Thrive with Little Data for Image Emotion Prediction.- Flatter Minima of Loss Landscapes Correspond with Strong Corruption Robustness.- Restoring Noisy Images using Dual-tail Encoder-Decoder Signal Separation Network.- Utilizing Deep Incomplete Classifiers To Implement Semantic Clustering For Killer Whale Photo Identification Data.- FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection.- C2F-CHART: A Curriculum Learning Approach to Chart Classification.- Vision DualGNN: Semantic Graph is Not Only You Need.- Enhancing Graph-based Clustering Based on the Regularity Lemma.- IPD: Scalable Clustering with Incremental Prototypes.- Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation.- Adaptive Graph-based Manifold Learning for Gene Selection.
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling.- Deep Evidential Active Learning with Uncertainty-Aware Determinantal Point Process.- Knowledge Distillation in Deep Networks under a Constrained Query Budget.- Adabot: An Adaptive Trading Bot using an Ensemble of Phase-specific Few-shot Learners to Adapt to the Changing Market Dynamics.- Uncertainty in Ambiguity of Data.- When Uncertainty-based Active Learning May Fail.- Customizable and Programmable Deep Learning.- SegXAL: Explainable Active Learning for semantic segmentation in driving scene scenarios.- AMC-OA: Adaptive Multi-Scale Convolutional Networks with Optimized Attention for Temporal Action Localization.- Comparative Analysis Of Pretrained Models for Text Classification, Generation and Summarization : A Detailed Analysis.- Predicting Judgement Outcomes from Legal Case File Summaries with Explainable Approach.- Multi-view Ensemble Clustering-based Podcast Recommendation in Indian Regional Setting.- Privacy-Preserving Ensemble Learning using Fully Homomorphic Encryption.- Capturing Temporal Components for Time Series Classification.- Hierarchical Transfer Multi-task Learning Approach for Scene Classification.- Deep Prompt Multi-task Network for Abuse Language Detection.- All mistakes are not equal: Comprehensive Hierarchy Aware Multilabel Predictions (CHAMP).- IDAL: Improved Domain Adaptive Learning for Natural Images Dataset.- Large Multimodal Models Thrive with Little Data for Image Emotion Prediction.- Flatter Minima of Loss Landscapes Correspond with Strong Corruption Robustness.- Restoring Noisy Images using Dual-tail Encoder-Decoder Signal Separation Network.- Utilizing Deep Incomplete Classifiers To Implement Semantic Clustering For Killer Whale Photo Identification Data.- FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection.- C2F-CHART: A Curriculum Learning Approach to Chart Classification.- Vision DualGNN: Semantic Graph is Not Only You Need.- Enhancing Graph-based Clustering Based on the Regularity Lemma.- IPD: Scalable Clustering with Incremental Prototypes.- Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation.- Adaptive Graph-based Manifold Learning for Gene Selection.
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