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Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, December 15, 2024, Proceedings, Part III [Mīkstie vāki]

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  • Formāts: Paperback / softback, 474 pages, height x width: 235x155 mm, 136 Illustrations, color; 6 Illustrations, black and white; XXXVII, 474 p. 142 illus., 136 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15303
  • Izdošanas datums: 05-Dec-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 303178121X
  • ISBN-13: 9783031781216
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  • Cena: 68,33 €*
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Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, December 15, 2024, Proceedings, Part IIIPalielināt
  • Formāts: Paperback / softback, 474 pages, height x width: 235x155 mm, 136 Illustrations, color; 6 Illustrations, black and white; XXXVII, 474 p. 142 illus., 136 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15303
  • Izdošanas datums: 05-Dec-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 303178121X
  • ISBN-13: 9783031781216
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031781216.html
Keywords:
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 15, 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.
Deep Multi-order Context-aware Kernel Network for Multi-label
Classification.- Classifier Enhanced Deep Learning Model for Erythroblast
Differentiation with Limited Data.- PiExtract: An End-to-End Data Extraction
pipeline for Pie-Charts.- Machine Learning Solutions for Predicting
Bankruptcy  in Indian Firms.- Efficient Object Detection via Fine-grained
Regularization with Global Initialization.- On Trace of PGD-Like Adversarial
Attacks.- CAB-KWS: Contrastive Augmentation: An Unsupervised Learning
Approach for Keyword Spotting in Speech Technology.- Deep Learning in
Automated Worm Identification and Tracking for C. Elegan Mating Behaviour
Analysis.- Interactive-Time Text-Guided Editing of 3D Face.- Unlearning
Vision Transformers without Retaining Data via Low-Rank Decompositions.-
gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using
Custom Kernel Integrated Deep Learning Method.- Neural-Code PIFu:
High-Fidelity Single Image 3D Human Reconstruction via Neural Code
Integration.- Sea-ShipNet: Detect Any Ship in SAR Images.- Semantic
Correlation Adaptation for Union-Set Multi-Label Image Recognition.- FedSC:
Federated Generalized Face Anti-Spoofing via Shuffled Codebook.- LoHoSC: Low
Order High Order Style Consistency for Syn-to-Real Domain Generalized
Semantic Segmentation.- Incorporating Spatial Locality into Self-Attention
for Training Vision Transformer on Small-Scale Datasets.- Cross-Domain
Calibration and Boundary Denoising Network for Weakly Supervised Semantic
Segmentation.- EFLLD-NET: Enhancing Few-Shot Learning With Local
Descriptors.- Using Multiscale Information for Improved Optimization-based
Image Attribution.- Split-DNN Computing for Video Analytics.- Task-Aware
Local Descriptors Reconstruction Network for Few-Shot Find-Grained Image
Classification.- TRIGS: Trojan Identification from Gradient-based
Signatures.- Multifaceted Anchor Nodes Attack on Graph Neural Networks: A
Budget-efficient Approach.- Causal Attentive Group Recommendation.- E2DAS: An
Efficient Equivariant Dynamic Aggregation Saliency Model for Omnidirectional
Images.- FewConv: Efficient variant convolution for few-shot image
generation.- FixPix: Fixing Bad Pixels using Deep Learning.- Real-world
Coarse to Fine-Grained Source-Free Multidomain Adaptation.