LEADER 03790nam 22006375 450 001 9910983029903321 005 20250508051717.0 010 $a9789819790036$b(electronic bk.) 010 $z9789819790029 024 7 $a10.1007/978-981-97-9003-6 035 $a(MiAaPQ)EBC31784479 035 $a(Au-PeEL)EBL31784479 035 $a(CKB)36590258700041 035 $a(DE-He213)978-981-97-9003-6 035 $a(EXLCZ)9936590258700041 100 $a20241116d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHuman Activity Recognition and Anomaly Detection $e4th International Workshop, DL-HAR 2024, and First International Workshop, ADFM 2024, Held in Conjunction with IJCAI 2024, Jeju, South Korea, August 3?9, 2024, Revised Selected Papers /$fedited by Kuan-Chuan Peng, Yizhou Wang, Ziyue Li, Zhenghua Chen, Jianfei Yang, Sungho Suh, Min Wu 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (156 pages) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v2201 311 08$aPrint version: Peng, Kuan-Chuan Human Activity Recognition and Anomaly Detection Singapore : Springer,c2024 9789819790029 327 $a -- 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. 330 $aThis book constitutes the refereed proceedings of the 4th International and First International Workshop on Human Activity Recognition and Anomaly Detection, Conjunction with IJCAI 2024, held in Jeju, South Korea, during August 3?9, 2024. The 9 full papers included in this book were carefully reviewed and selected from 14 submissions. They were organized in topical sections as follows: Anomaly Detection with Foundation Models and Deep Learning for Human Activity Recognition. 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v2201 606 $aArtificial intelligence 606 $aCompilers (Computer programs) 606 $aComputer simulation 606 $aArtificial Intelligence 606 $aCompilers and Interpreters 606 $aComputer Modelling 615 0$aArtificial intelligence. 615 0$aCompilers (Computer programs) 615 0$aComputer simulation. 615 14$aArtificial Intelligence. 615 24$aCompilers and Interpreters. 615 24$aComputer Modelling. 676 $a006.3 700 $aPeng$b Kuan-Chuan$01368114 701 $aWang$b Yizhou$01785423 701 $aLi$b Ziyue$01785424 701 $aChen$b Zhenghua$01785425 701 $aYang$b Jianfei$01785426 701 $aSuh$b Sungho$01785427 701 $aWu$b Min$0909255 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910983029903321 996 $aHuman Activity Recognition and Anomaly Detection$94316967 997 $aUNINA LEADER 01096nas 2200397 c 450 001 9910892342703321 005 20260127110532.0 011 $a2730-020X 035 $a(DE-599)ZDB3078584-4 035 $a(OCoLC)1368835610 035 $a(DE-101)1238085563 035 $a(CKB)5450000000346051 035 $a(DE-599)3078584-4 035 $a(EXLCZ)995450000000346051 100 $a20210803a20179999 |y | 101 0 $aslo 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReflexie$ekompendium teo?rie a praxe podnikania 210 31$aRuz?omberok$cVERBUM - vydavatel?stvo Katoli?ckej univerzity$d[2017]- 215 $aOnline-Ressource 300 $aGesehen am 01.12.2021 311 08$a2585-7428 517 1 $aReflections 517 1 $aCompendium of business theory and practice 608 $aZeitschrift$2gnd-content 676 $a330 801 0$b0012 801 1$bDE-101 801 2$b9999 906 $aJOURNAL 912 $a9910892342703321 996 $aReflexie$94439286 997 $aUNINA