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 3–9, 2024, Revised Selected Papers / / edited by Kuan-Chuan Peng, Yizhou Wang, Ziyue Li, Zhenghua Chen, Jianfei Yang, Sungho Suh, Min Wu

• Autore: Peng Kuan-Chuan
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
• Edizione: 1st ed. 2025.
• Descrizione fisica: 1 online resource (156 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Compilers (Computer programs); Computer simulation; Artificial Intelligence; Compilers and Interpreters; Computer Modelling
• Altri autori: WangYizhou LiZiyue ChenZhenghua YangJianfei SuhSungho WuMin
• Nota di contenuto: -- 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.
• Sommario/riassunto: 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 3–9, 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.
• Titolo autorizzato: Human Activity Recognition and Anomaly Detection
• ISBN: 9789819790036; 9789819790029
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910983029903321
• Lo trovi qui: Univ. Federico II
• Serie: Communications in Computer and Information Science, . 1865-0937 ; ; 2201