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Mask attention-guided graph convolution layer for weakly supervised temporal action detection
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DOI：10.1007/s11042-021-11768-1.png)
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Graph convolutional network has been applied for temporal action detection. However, it is performed on the temporal context from the local view of segments containing incomplete actions, which weakens its ability to learn global representation. To alleviate this problem, a novel mask attention-guided graph convolution layer with the cooperation of local and global views is introduced. The graph weighted by feature similarity between segments is regarded as local view, while the global view takes the overall action distribution of the video as a guide to establish the relevance of all actions in a video. Via adding such a global guide, graph convolutional network can learn more discriminative spatio-temporal correlation representation, therefore, we propose mask attention-guided graph convolution layer for weakly supervised temporal action detection. Taking the segments features as the graph nodes, the mask attention-guided foreground-background graph and the transition-aware temporal mask graph are constructed, and then the segments association features and temporal context features are obtained, and finally cascaded and used for action detection. Experiments on the Thumos14 and Activitynet1.2 datasets achieve a mean average precision of 29.7% and 32.7% under the tIoU threshold 0.5, respectively. The results show that the proposed approach can effectively improve the performance of action detection.
Keyword:
Temporal action detection
Graph convolutional network
Mask attention
Overall action distribution
Temporal relation
Feature fusion
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3
论文数:
2.0W
被引数:
3.2W
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Deep snippet selective network for weakly supervised temporal action localization.png)
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