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

AvKuan-Chuan Peng,Yizhou Wang

Häftad, Engelska, 2024

Del i serien Communications in Computer and Information Science

744 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2024-11-17
Mått:155 x 235 x 10 mm
Vikt:260 g
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:147
Förlag:Springer Verlag, Singapore
ISBN:9789819790029

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Programmeringsböcker inom Data och IT

Innehållsförteckning

.- 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.