PcmNet: Position-Sensitive Context Modeling Network for Temporal Action Localization

• URL: http://arxiv.org/abs/2103.05270v1
• Date: Tue, 9 Mar 2021 07:34:01 GMT
• Title: PcmNet: Position-Sensitive Context Modeling Network for Temporal Action Localization
• Authors: Xin Qin, Hanbin Zhao, Guangchen Lin, Hao Zeng, Songcen Xu, Xi Li
• Abstract summary: We propose a temporal-position-sensitive context modeling approach to incorporate both positional and semantic information for more precise action localization. We achieve state-of-the-art performance on both two challenging datasets, THUMOS-14 and ActivityNet-1.3.
• Score: 11.685362686431446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal action localization is an important and challenging task that aims to locate temporal regions in real-world untrimmed videos where actions occur and recognize their classes. It is widely acknowledged that video context is a critical cue for video understanding, and exploiting the context has become an important strategy to boost localization performance. However, previous state-of-the-art methods focus more on exploring semantic context which captures the feature similarity among frames or proposals, and neglect positional context which is vital for temporal localization. In this paper, we propose a temporal-position-sensitive context modeling approach to incorporate both positional and semantic information for more precise action localization. Specifically, we first augment feature representations with directed temporal positional encoding, and then conduct attention-based information propagation, in both frame-level and proposal-level. Consequently, the generated feature representations are significantly empowered with the discriminative capability of encoding the position-aware context information, and thus benefit boundary detection and proposal evaluation. We achieve state-of-the-art performance on both two challenging datasets, THUMOS-14 and ActivityNet-1.3, demonstrating the effectiveness and generalization ability of our method.

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