Uncovering the Unseen: Discover Hidden Intentions by Micro-Behavior Graph Reasoning

TL;DR

This paper proposes a novel Hidden Intention Discovery task that leverages micro-behavior analysis and graph reasoning to identify concealed intentions in abnormal behaviors, supported by a new dataset and improved performance.

Contribution

It introduces the HID task, constructs a new dataset with annotations for theft scenarios, and develops a graph-based method that outperforms existing approaches.

Findings

The proposed method improves HID accuracy by 9.9% over the state-of-the-art.

Micro-behaviors like gaze and facial expressions are key indicators of hidden intentions.

Graph reasoning effectively uncovers concealed intentions in complex scenarios.

Abstract

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.