1 code implementation • 22 Jun 2022 • Jia-Run Du, Jia-Chang Feng, Kun-Yu Lin, Fa-Ting Hong, Xiao-Ming Wu, Zhongang Qi, Ying Shan, Wei-Shi Zheng
Accordingly, we first exclude these surely non-existent categories by a complementary learning loss.
1 code implementation • Proceedings of the 29th ACM International Conference on Multimedia 2021 • Fa-Ting Hong, Jia-Chang Feng, Dan Xu, Ying Shan, Wei-Shi Zheng
In this work, we argue that the features extracted from the pretrained extractor, e. g., I3D, are not the WS-TALtask-specific features, thus the feature re-calibration is needed for reducing the task-irrelevant information redundancy.
Weakly-supervised Temporal Action Localization Weakly Supervised Temporal Action Localization
2 code implementations • 27 Jul 2021 • Fa-Ting Hong, Jia-Chang Feng, Dan Xu, Ying Shan, Wei-Shi Zheng
In this work, we argue that the features extracted from the pretrained extractor, e. g., I3D, are not the WS-TALtask-specific features, thus the feature re-calibration is needed for reducing the task-irrelevant information redundancy.
Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1
1 code implementation • CVPR 2021 • Jia-Chang Feng, Fa-Ting Hong, Wei-Shi Zheng
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations.