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Human Activity Recognition and Anomaly Detection: 4th International Workshop, DL-HAR 2024, and First International Workshop, ADFM 2024, Held in Conjunction with IJCAI 2024, Jeju, South Korea, August 39, 2024, Revised Selected Papers 2024 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 147 pages, kõrgus x laius: 235x155 mm, 50 Illustrations, color; 1 Illustrations, black and white; XIV, 147 p. 51 illus., 50 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 2201
  • Ilmumisaeg: 17-Nov-2024
  • Kirjastus: Springer Nature
  • ISBN-10: 9819790026
  • ISBN-13: 9789819790029
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Human Activity Recognition and Anomaly Detection: 4th International Workshop, DL-HAR 2024, and First International Workshop, ADFM 2024, Held in Conjunction with IJCAI 2024, Jeju, South Korea, August 39, 2024, Revised Selected Papers 2024 ed.Suurem pilt
  • Formaat: Paperback / softback, 147 pages, kõrgus x laius: 235x155 mm, 50 Illustrations, color; 1 Illustrations, black and white; XIV, 147 p. 51 illus., 50 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 2201
  • Ilmumisaeg: 17-Nov-2024
  • Kirjastus: Springer Nature
  • ISBN-10: 9819790026
  • ISBN-13: 9789819790029
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789819790029.html
Märksõnad:
This book constitutes the refereed proceedings of the 4th International and First International Workshop on Human Activity Recognition and Anomaly Detection, Conjunction with IJCAI 2024, held in Jeju, South Korea, during August 39, 2024.





The 9 full papers included in this book were carefully reviewed and selected from 14 submissions. They were organized in topical sections as follows: Anomaly Detection with Foundation Models and Deep Learning for Human Activity Recognition.

.- Anomaly Detection with Foundation Models.
.- GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection.
.- CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection.
.- DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learning.
.- Dual Memory-guided Probabilistic Model for Weakly-supervised Anomaly Detection.
.- Deep Learning for Human Activity Recognition.
.- Real-Time Human Action Prediction via Pose Kinematics.
.- Uncertainty Awareness for Unsupervised Domain Adaptation on Human Activity Recognition.
.- Deep Interaction Feature Fusion for Robust Human Activity Recognition.
.- How effective are Self-Supervised models for Contact Identification in Videos.
.- A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition.