Multiple Instance Weakly-Supervised Training Framework for Video Anomaly Detection

Ying Hu; Yuanyao Lu

doi:10.1007/978-3-031-94898-5_13

ScienceGate Book Chapters

BOOK-CHAPTER

Multiple Instance Weakly-Supervised Training Framework for Video Anomaly Detection

Ying Hu Yuanyao Lu

Year: 2025 Lecture notes in networks and systems Pages: 163-180 Publisher: Springer International Publishing

DOI: 10.1007/978-3-031-94898-5_13

Get Full-Text PDF Get Analytical Report

Keywords:

Anomaly detection Training (meteorology) Computer science Artificial intelligence Anomaly (physics) Machine learning Pattern recognition (psychology) Geography Physics Meteorology

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.44

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Artificial Immune Systems Applications

Physical Sciences → Engineering → Biomedical Engineering

Multiple Instance Weakly-Supervised Training Framework for Video Anomaly Detection

Metrics

Topics

Related Documents

A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection

Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection

Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection

Normality Guided Multiple Instance Learning for Weakly Supervised Video Anomaly Detection

MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection