LEADER 05008nam 22006135 450 001 9910983480203321 005 20241202151943.0 010 $a9783031781070 010 $a3031781074 024 7 $a10.1007/978-3-031-78107-0 035 $a(MiAaPQ)EBC31810908 035 $a(Au-PeEL)EBL31810908 035 $a(CKB)36738990600041 035 $a(DE-He213)978-3-031-78107-0 035 $a(OCoLC)1477225573 035 $a(EXLCZ)9936738990600041 100 $a20241202d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition $e27th International Conference, ICPR 2024, Kolkata, India, December 1?5, 2024, Proceedings, Part I /$fedited by Apostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (507 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15301 311 08$a9783031781063 311 08$a3031781066 327 $aSemi-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. 330 $aThe multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1?5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15301 606 $aComputer vision 606 $aMachine learning 606 $aComputer Vision 606 $aMachine Learning 615 0$aComputer vision. 615 0$aMachine learning. 615 14$aComputer Vision. 615 24$aMachine Learning. 676 $a006.37 700 $aAntonacopoulos$b Apostolos$0885419 701 $aChaudhuri$b Subhasis$0846530 701 $aChellappa$b Rama$0491442 701 $aLiu$b Cheng-Lin$0861045 701 $aBhattacharya$b Saumik$01782600 701 $aPal$b Umapada$01782601 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983480203321 996 $aPattern Recognition$94309011 997 $aUNINA