CTRN: Class-Temporal Relational Network for Action Detection

• URL: http://arxiv.org/abs/2110.13473v1
• Date: Tue, 26 Oct 2021 08:15:47 GMT
• Title: CTRN: Class-Temporal Relational Network for Action Detection
• Authors: Rui Dai, Srijan Das, Francois Bremond
• Abstract summary: We introduce an end-to-end network: Class-Temporal Network (CTRN) CTRN contains three key components: The Transform Representation Module, the Class-Temporal Module and the G-classifier. We evaluate CTR on three densely labelled datasets and achieve state-of-the-art performance.
• Score: 7.616556723260849
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Action detection is an essential and challenging task, especially for densely labelled datasets of untrimmed videos. There are many real-world challenges in those datasets, such as composite action, co-occurring action, and high temporal variation of instance duration. For handling these challenges, we propose to explore both the class and temporal relations of detected actions. In this work, we introduce an end-to-end network: Class-Temporal Relational Network (CTRN). It contains three key components: (1) The Representation Transform Module filters the class-specific features from the mixed representations to build graph-structured data. (2) The Class-Temporal Module models the class and temporal relations in a sequential manner. (3) G-classifier leverages the privileged knowledge of the snippet-wise co-occurring action pairs to further improve the co-occurring action detection. We evaluate CTRN on three challenging densely labelled datasets and achieve state-of-the-art performance, reflecting the effectiveness and robustness of our method.

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