mlsanityData to 14 Oct 2026

Multi-source Multimodal Progressive Domain Adaption for Audio-Visual Deception Detection

arXiv 2508.12842 ↗First posted 18 Aug 2025cs.CV cs.MMcited by 0

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Abstract (arXiv)

This paper presents the winning approach for the 1st MultiModal Deception Detection (MMDD) Challenge at the 1st Workshop on Subtle Visual Computing (SVC). Aiming at the domain shift issue across source and target domains, we propose a Multi-source Multimodal Progressive Domain Adaptation (MMPDA) framework that transfers the audio-visual knowledge from diverse source domains to the target domain. By gradually aligning source and the target domain at both feature and decision levels, our method bridges domain shifts across diverse multimodal datasets. Extensive experiments demonstrate the effectiveness of our approach securing Top-2 place. Our approach reaches 60.43% on accuracy and 56.99% on F1-score on competition stage 2, surpassing the 1st place team by 5.59% on F1-score and the 3rd place teams by 6.75% on accuracy. Our code is available at https://github.com/RH-Lin/MMPDA.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 43 references that are in the map. First posted on arXiv 18 Aug 2025.

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References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  3. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,200
  4. 2017Learning to Generalize: Meta-Learning for Domain Generalizationcited by 763
  5. 2021Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalizationcited by 144
  6. 2020Towards Universal Representation Learning for Deep Face Recognitioncited by 78
  7. 2020Maximum Density Divergence for Domain Adaptationcited by 76
  8. 2020Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Surveycited by 47
  9. 2025Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Modelscited by 23
  10. 2023Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learningcited by 14
  11. 2024More is Better: Deep Domain Adaptation with Multiple Sourcescited by 9
  12. 2020Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptationcited by 7
4 more
  1. 2021Deception Detection in Group Video Conversations using Dynamic Interaction Networkscited by 6
  2. 2024Benchmarking Cross-Domain Audio-Visual Deception Detectioncited by 6
  3. 2025SVC 2025: the First Multimodal Deception Detection Challengecited by 5
  4. 2021Constructing Robust Emotional State-based Feature with a Novel Voting Scheme for Multi-modal Deception Detection in Videoscited by 1

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