English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper introduces a new and challenging Hidden Intention Discovery (HID)task. Unlike existing intention recognition tasks, which are based on obviousvisual representations to identify common intentions for normal behavior, HIDfocuses on discovering hidden intentions when humans try to hide theirintentions for abnormal behavior. HID presents a unique challenge in thathidden intentions lack the obvious visual representations to distinguish themfrom normal intentions. Fortunately, from a sociological and psychologicalperspective, we find that the difference between hidden and normal intentionscan be reasoned from multiple micro-behaviors, such as gaze, attention, andfacial expressions. Therefore, we first discover the relationship betweenmicro-behavior and hidden intentions and use graph structure to reason abouthidden intentions. To facilitate research in the field of HID, we alsoconstructed a seminal dataset containing a hidden intention annotation of atypical theft scenario for HID. Extensive experiments show that the proposednetwork improves performance on the HID task by 9.9\% over the state-of-the-artmethod SBP.

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