Mahmud / Doborjeh / Tanveer | Neural Information Processing | Buch | 978-981-966598-3 | sack.de

Buch, Englisch, 459 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 744 g

Reihe: Lecture Notes in Computer Science

Mahmud / Doborjeh / Tanveer

Neural Information Processing

31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2-6, 2024, Proceedings, Part VIII
Erscheinungsjahr 2025
ISBN: 978-981-966598-3
Verlag: Springer Nature Singapore

31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2-6, 2024, Proceedings, Part VIII

Buch, Englisch, 459 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 744 g

Reihe: Lecture Notes in Computer Science

ISBN: 978-981-966598-3
Verlag: Springer Nature Singapore


The eleven-volume set LNCS 15286-15296 constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024.The 318 regular papers presented in the proceedings set were carefully reviewed and selected from 1301 submissions. They focus on four main areas, namely: theory and algorithms; cognitive neurosciences; human-centered computing; and applications.

Mahmud / Doborjeh / Tanveer Neural Information Processing jetzt bestellen!

Zielgruppe


Research

Weitere Infos & Material


Generalized Multi-Scale Separable EPI Information for Light Field Image Super-Resolution.- ForgeryGPT: Cross-domain Face Forgery Detection using Large Vision-Language Models.- MFCA: Multimodal Object Detection based on Feature Calibration and Aggregation.- Omnidirectional Linear Attention Module for Penalty Shootout Video Understanding.- UOA-RCNN: detect anything with Unknown Object Aware RCNN.- WOODWIND: Few-shot Object Detector with Knowledge Distillation.- LITE: A Paradigm Shift in Multi-Object Tracking with Efficient ReID Feature Integration.- LSReGen: Large-Scale Regional Generator via Backward Guidance Framework.- AAFE-Net:Agent-Based Adaptive Feature Enhanced Network for Leather Defect Detection.- AA-RPN: Adaptive Anchor-based Region Proposal Network for Remote Sensing Object Detection.- Asterisk sparse convolutional networks for 3D object detection Shifted Window Fourier Transform And Retention For Image Captioning.- FOPS-V: Feature-Aware Optimization and Parallel Scale Fusion for 3D Human Reconstruction in Video.- PEBTrack: A Performance-Efficiency Balance Tracker for Aerial Scenario.- Multi-Scale Spatial-Angular Information Aggregation Network for Image Semantic Segmentation.- Enhancing Semi-Supervised Medical Image Segmentation with Asymmetric and Adversarial Cooperative Training.- Face Super-Resolution Using Covariation-Guided Orthonormalized Partial Least Squares.- SegDaemon: Actively Protecting Semantic Segmentation Models Against Intellectual Property Infringement.- Contrastive Diffusion Generative Adversarial Network for Generalized Zero-Shot Learning.- MASR: Efficient Multi-Attention Network For Single Image Super-Resolution.- YOLO-pdd: A Novel Multi-scale PCB Defect Detection Method Using Deep Representations with Sequential Images.- Correlation-Guided Image-to-Video Transfer Learning for Video Recognition.- Contrastive learning for free-view image compression network.- Enriching Degradation Features for Fundus Image Enhancement via Multi-colour Dynamic Filter Network.- -Code: Simple Temporal Latent Code for Efficient Dynamic View Synthesis.- A Semantic Segmentation Method for Skin Lesion Images Based on ViT.- Multi-scale Feature Edge Enhancement for Multi-view Stereo.- Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation.- How to Efficiently Use Color and Temporal Information for Video Understanding.- Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement.- BBLMixSTE:Barbell Tokenizer for Autism Spectrum Disorder Video Reconstruction.



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.