  Affective Behavior Analysis is an important part in human-computer
interaction. Existing multi-task affective behavior recognition methods suffer
from the problem of incomplete labeled datasets. To tackle this problem, this
paper presents a semi-supervised model with a mean teacher framework to
leverage additional unlabeled data. To be specific, a multi-task model is
proposed to learn three different kinds of facial affective representations
simultaneously. After that, the proposed model is assigned to be student and
teacher networks. When training with unlabeled data, the teacher network is
employed to predict pseudo labels for student network training, which allows it
to learn from unlabeled data. Experimental results showed that our proposed
method achieved much better performance than baseline model and ranked 4th in
both competition track 1 and track 2, and 6th in track 3, which verifies that
the proposed network can effectively learn from incomplete datasets.
