Affective Behavior Analysis using Task-adaptive and AU-assisted Graph Network
<|reference_start|>Affective Behavior Analysis using Task-adaptive and AU-assisted Graph Network: In this paper, we present our solution and experiment result for the Multi-Task Learning Challenge of the 7th Affective Behavior Analysis in-the-wild(ABAW7) Competition. This challenge consists of three tasks: action unit detection, facial expression recognition, and valance-arousal estimation. We address the research problems of this challenge from three aspects: 1)For learning robust visual feature representations, we introduce the pre-trained large model Dinov2. 2) To adaptively extract the required features of eack task, we design a task-adaptive block that performs cross-attention between a set of learnable query vectors and pre-extracted features. 3) By proposing the AU-assisted Graph Convolutional Network(AU-GCN), we make full use of the correlation information between AUs to assist in solving the EXPR and VA tasks. Finally, we achieve the evaluation measure of \textbf{1.2542} on the validation set provided by the organizers.<|reference_end|>
@article{li2024affective,
    title={Affective Behavior Analysis using Task-adaptive and AU-assisted Graph
  Network},
    author={Xiaodong Li, Wenchao Du and Hongyu Yang},
    journal={arXiv preprint arXiv:2407.11663},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11663},
    primaryClass={cs.CV}
}