MRSN: Multi-Relation Support Network for Video Action Detection

• URL: http://arxiv.org/abs/2304.11975v1
• Date: Mon, 24 Apr 2023 10:15:31 GMT
• Title: MRSN: Multi-Relation Support Network for Video Action Detection
• Authors: Yin-Dong Zheng, Guo Chen, Minglei Yuan, Tong Lu
• Abstract summary: Action detection is a challenging video understanding task requiring modeling relations. We propose a novel network called Multi-temporallation Supportarity Network. Our experiments demonstrate that modeling relations separately and performing relation-level interactions can achieve state-of-the-art results.
• Score: 15.82531313330869
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Action detection is a challenging video understanding task, requiring modeling spatio-temporal and interaction relations. Current methods usually model actor-actor and actor-context relations separately, ignoring their complementarity and mutual support. To solve this problem, we propose a novel network called Multi-Relation Support Network (MRSN). In MRSN, Actor-Context Relation Encoder (ACRE) and Actor-Actor Relation Encoder (AARE) model the actor-context and actor-actor relation separately. Then Relation Support Encoder (RSE) computes the supports between the two relations and performs relation-level interactions. Finally, Relation Consensus Module (RCM) enhances two relations with the long-term relations from the Long-term Relation Bank (LRB) and yields a consensus. Our experiments demonstrate that modeling relations separately and performing relation-level interactions can achieve and outperformer state-of-the-art results on two challenging video datasets: AVA and UCF101-24.

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