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

被引：1

作者：

Zhou, Zhuo ^{[1
]}

Liu, Wenxuan ^{[1
]}

Xu, Danni ^{[2
]}

Wang, Zheng ^{[3
]}

Zhao, Jian ^{[4
]}

机构：

[1] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China

[2] Natl Univ Singapore, Sch Comp, Singapore, Singapore

[3] Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Wuhan, Peoples R China

[4] Intelligent Game & Decis Lab, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2023 | 2023年

基金：

中国国家自然科学基金;

关键词：

Intention Recognition; Hidden Intention Discovery; Graph Reasoning; Gaze Detection;

D O I：

10.1145/3581783.3611892

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 microbehaviors, 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.

引用

页码：6623 / 6633

页数：11