TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Weakly Supervised Action Localization	THUMOS14	AUMN	avg-mAP (0.1-0.5)	52.1	# 6
Weakly Supervised Action Localization	THUMOS14	AUMN	avg-mAP (0.3-0.7)	32.4	# 7
Weakly Supervised Action Localization	THUMOS14	AUMN	avg-mAP (0.1:0.7)	41.5	# 8

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Action Unit Memory Network for Weakly Supervised Temporal Action Localization

CVPR 2021 · Wang Luo, Tianzhu Zhang, Wenfei Yang, Jingen Liu, Tao Mei, Feng Wu, Yongdong Zhang ·

Weakly supervised temporal action localization aims to detect and localize actions in untrimmed videos with only video-level labels during training. However, without frame-level annotations, it is challenging to achieve localization completeness and relieve background interference. In this paper, we present an Action Unit Memory Network (AUMN) for weakly supervised temporal action localization, which can mitigate the above two challenges by learning an action unit memory bank. In the proposed AUMN, two attention modules are designed to update the memory bank adaptively and learn action units specific classifiers. Furthermore, three effective mechanisms (diversity, homogeneity and sparsity) are designed to guide the updating of the memory network. To the best of our knowledge, this is the first work to explicitly model the action units with a memory network. Extensive experimental results on two standard benchmarks (THUMOS14 and ActivityNet) demonstrate that our AUMN performs favorably against state-of-the-art methods. Specifically, the average mAP of IoU thresholds from 0.1 to 0.5 on the THUMOS14 dataset is significantly improved from 47.0% to 52.1%.