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Modeling Sub-Actions for Weakly Supervised Temporal Action Localization
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DOI：10.1109/TIP.2021.3078324.png)
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摘要
En 中文
As a challenging task of high-level video understanding, weakly supervised temporal action localization has attracted more attention recently. Due to the usage of video-level category labels, this task is usually formulated as the task of classification, which always suffers from the contradiction between classification and detection. In this paper, we describe a novel approach to alleviate the contradiction for detecting more complete action instances by explicitly modeling sub-actions. Our method makes use of three innovations to model the latent sub-actions. First, our framework uses prototypes to represent sub-actions, which can be automatically learned in an end-to-end way. Second, we regard the relations among sub-actions as a graph, and construct the correspondences between sub-actions and actions by the graph pooling operation. Doing so not only makes the sub-actions inter-dependent to facilitate the multi-label setting, but also naturally use the video-level labels as weak supervision. Third, we devise three complementary loss functions, namely, representation loss, balance loss and relation loss to ensure the learned sub-actions are diverse and have clear semantic meanings. Experimental results on THUMOS14 and ActivityNet1.3 datasets demonstrate the effectiveness of our method and superior performance over state-of-the-art approaches.
Keyword:
Location awareness
Hidden Markov models
Proposals
Task analysis
Prototypes
Annotations
Deep learning
Weakly supervised learning
temporal action localization
sub-action modeling
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13.7
论文数:
1.0W
被引数:
8.4W
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