This paper introduces a new and challenging Hidden Intention Discovery (HID) task. Unlike existing intention recognition tasks, which are based on obvious visual representations to identify common intentions for normal behavior, HID focuses on discovering hidden intentions when humans try to hide their intentions for abnormal behavior. HID presents a unique challenge in that hidden intentions lack the obvious visual representations to distinguish them from normal intentions. Fortunately, from a sociological and psychological perspective, we find that the difference between hidden and normal intentions can be reasoned from multiple micro-behaviors, such as gaze, attention, and facial expressions. Therefore, we first discover the relationship between micro-behavior and hidden intentions and use graph structure to reason about hidden intentions. To facilitate research in the field of HID, we also constructed a seminal dataset containing a hidden intention annotation of a typical theft scenario for HID. Extensive experiments show that the proposed network improves performance on the HID task by 9.9\% over the state-of-the-art method SBP.

该论文介绍了一项新的具有挑战性的隐藏意图发现（HID）任务，重点关注人们试图隐藏其异常行为意图的情况下的隐藏意图发现。论文首先确定了微行为和隐藏意图之间的关系，并使用图结构推理隐藏意图。实验证明，所提出的网络在HID任务中的表现比最先进的方法SBP提高了9.9%。

揭示未知：通过微行为图推理发现隐藏的意图