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.

该研究提出了一种名为 MRSN 的神经网络模型，利用 Actor-Context 和 Actor-Actor Relation Encoder 分别建模，并通过 Relation Support Encoder 计算二者的支持并进行关系级互动，最终通过 Relation Consensus Module 将这两个关系与长期关系相融合，成功地解决了动作检测这一具有挑战性的视频理解任务。