  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.
