Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection-Reference-Cited by-同舟云学术

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Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Zhang Chen¹,Li Guorong²,Qi Yuankai³,Wang Shuhui¹,Qing Laiyun²,Huang Qingming¹,Yang Ming-Hsuan⁴

Affiliation:

1. Institute of Information Engineering, CAS,State Key Laboratory of Information Security

2. School of Computer Science and Technology, University of Chinese Academy of Sciences

3. Australian Institute for Machine Learning, The University of Adelaide

4. University of California,Merced

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10204494.pdf?arnumber=10204494

Reference42 articles.

1. Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization

2. Self-Training With Noisy Student Improves ImageNet Classification

3. Self-Training Multi-Sequence Learning with Transformer for Weakly Supervised Video Anomaly Detection

4. Not only look, but also listen: Learning multimodal violence detection under weak supervision;wu;ECCV,0

5. Future Frame Prediction for Anomaly Detection - A New Baseline

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