Uncovering the Unseen: Discover Hidden Intentions by Micro-Behavior Graph Reasoning
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 29 Aug 2023.
Builds on
17 of 51 references.
References · 17
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
- 2018SlowFast Networks for Video Recognitioncited by 1,867
- 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,068
- 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 902
- 2018TSM: Temporal Shift Module for Efficient Video Understandingcited by 856
- 2020X3D: Expanding Architectures for Efficient Video Recognitioncited by 637
- 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
- 2020SpeedNet: Learning the Speediness in Videoscited by 171
- 2014Cross-view Action Modeling, Learning and Recognitioncited by 134
- 2020Detecting Attended Visual Targets in Videocited by 77
- 2020Spatiotemporal Relationship Reasoning for Pedestrian Intent Predictioncited by 71
5 more
- 2020Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learningcited by 66
- 2021AGENT: A Benchmark for Core Psychological Reasoningcited by 42
- 2020Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendationcited by 18
- 20203D CNNs with Adaptive Temporal Feature Resolutionscited by 15
- 2022Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Modelscited by 14
Built on by
Citing papers per year: 2025: 2.
2 papers.