5 papers
DeceptionX: From Multimodal Evidence to Explainable Deception Detection
Jiayu Zhang, Shuo Ye, Jiajian Huang +8
Deception detection is a critical and highly challenging task within affective computing and behavioral analysis. Existing deep learning methods typically treat this task as a stra…
Retrieving to Recover: Towards Incomplete Audio-Visual Question Answering via Semantic-consistent Purification
Jiayu Zhang, Shuo Ye, Qilang Ye +3
Recent Audio-Visual Question Answering (AVQA) methods have advanced significantly. However, most AVQA methods lack effective mechanisms for handling missing modalities, suffering f…
SVC 2026: the Second Multimodal Deception Detection Challenge and the First Domain Generalized Remote Physiological Measurement Challenge
Dongliang Zhu, Zhiyi Niu, Bo Zhao +14
Subtle visual signals, although difficult to perceive with the naked eye, contain important information that can reveal hidden patterns in visual data. These signals play a key rol…
DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning
Jiajian Huang, Dongliang Zhu, Zitong YU +4
Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security. In these high-stakes settings, investigators need verif…
SVC 2025: the First Multimodal Deception Detection Challenge
Xun Lin, Xiaobao Guo, Taorui Wang +5
Deception detection is a critical task in real-world applications such as security screening, fraud prevention, and credibility assessment. While deep learning methods have shown p…