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Found 4 papers, 2 papers with code
Benchmarking Cross-Domain Audio-Visual Deception Detection
Automated deception detection is crucial for assisting humans in accurately assessing truthfulness and identifying deceptive behavior.
Improving Concept Alignment in Vision-Language Concept Bottleneck Models
Concept Bottleneck Models (CBM) map the input image to a high-level human-understandable concept space and then make class predictions based on these concepts.
Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning
Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively.
Flexible-modal Deception Detection with Audio-Visual Adapter
Detecting deception by human behaviors is vital in many fields such as custom security and multimedia anti-fraud.
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