  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.
