LEADER 05052nam 22006015 450 001 996630870703316 005 20250626164208.0 010 $a9783031781285 010 $a3031781287 024 7 $a10.1007/978-3-031-78128-5 035 $a(CKB)36701919600041 035 $a(MiAaPQ)EBC31808152 035 $a(Au-PeEL)EBL31808152 035 $a(DE-He213)978-3-031-78128-5 035 $a(EXLCZ)9936701919600041 100 $a20241130d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition $e27th International Conference, ICPR 2024, Kolkata, India, December 1?5, 2024, Proceedings, Part IV /$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 (0 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15304 311 08$a9783031781278 311 08$a3031781279 327 $aDeepEMD: A Transformer-based Fast Estimation of the Earth Mover?s Distance -- Equivariant Neural Networks for TEM Virus Images Improves Data Efficiency -- AI Based Story Generation -- Deep learning models for inference on compressed signals with known or unknown measurement matrix -- Training point-based deep learning networks for forest segmentation with synthetic data -- Brain Age Estimation using Self-attention based Convolutional Neural Network -- IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence -- Interpretable Deep Graph-level Clustering: A Prototype-based Approach -- A Saliency-Aware NR-IQA Method by Fusing Distortion Class Information -- A Guided Input Sampling-based Perturbative Approach for Explainable AI in Image-based Application -- Multi-target Attention Dispersion Adversarial Attack against Aerial Object Detector -- Mask-TS Net: Mask Temperature Scaling Uncertainty Calibration for Polyp Segmentation -- Label-expanded Feature Debiasing for Single Domain Generalization -- Infrared and Visible Image Fusion Based on CNN and Transformer Cross-Interaction with Semantic Modulations -- Mining Long Short-Term Evolution Patterns for Temporal Knowledge Graph Reasoning -- Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging -- A Weighted Discrete Wavelet Transform-based Capsule Network for Malware Classification -- Data-driven Spatiotemporal Aware Graph Hybrid-hop Transformer Network for Traffic Flow Forecasting -- Automatic Diagnosis Model of Gastrointestinal Diseases Based on Tongue Images -- TinyConv-PVT: A Deeper Fusion Model of CNN and Transformer for Tiny Dataset -- SCAD-Net: Spatial-Channel Attention and Depth-map Analysis Network for Face Anti-Spoofing -- Next Generation Loss Function for Image Classification -- NAOL: NeRF-Assisted Omnidirectional Localization -- EdgeConvFormer: an unsupervised anomaly detection method for multivariate time series -- Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels -- Hand over face gesture classification with feature driven vision transformer and supervised contrastive learning -- TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering -- GraFix: A Graph Transformer with Fixed Attention based on the WL Kernel -- Multi-Modal Deep Emotion-Cause Pair Extraction for Video Corpus. 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,$x1611-3349 ;$v15304 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 $a996630870703316 996 $aPattern Recognition$94309011 997 $aUNISA