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Solution for 8th Competition on Affective & Behavior Analysis in-the-wild

arXiv 2503.11115 ↗First posted 14 Mar 2025cs.CVcited by 0

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Abstract (arXiv)

In this report, we present our solution for the Action Unit (AU) Detection Challenge, in 8th Competition on Affective Behavior Analysis in-the-wild. In order to achieve robust and accurate classification of facial action unit in the wild environment, we introduce an innovative method that leverages audio-visual multimodal data. Our method employs ConvNeXt as the image encoder and uses Whisper to extract Mel spectrogram features. For these features, we utilize a Transformer encoder-based feature fusion module to integrate the affective information embedded in audio and image features. This ensures the provision of rich high-dimensional feature representations for the subsequent multilayer perceptron (MLP) trained on the Aff-Wild2 dataset, enhancing the accuracy of AU detection.

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References · 21

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  10. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
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  12. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
9 more
  1. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  2. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  3. 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
  4. 2024Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyondcited by 60
  5. 20247th ABAW Competition: Multi-Task Learning and Compound Expression Recognitioncited by 53
  6. 2022XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learningcited by 31
  7. 2017Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusingcited by 28
  8. 2017Deep Structured Learning for Facial Action Unit Intensity Estimationcited by 17
  9. 2022Modality Mixer for Multi-modal Action Recognitioncited by 9

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