  We propose an audio-visual spatial-temporal deep neural network with: (1) a
visual block containing a pretrained 2D-CNN followed by a temporal
convolutional network (TCN); (2) an aural block containing several parallel
TCNs; and (3) a leader-follower attentive fusion block combining the
audio-visual information. The TCN with large history coverage enables our model
to exploit spatial-temporal information within a much larger window length
(i.e., 300) than that from the baseline and state-of-the-art methods (i.e., 36
or 48). The fusion block emphasizes the visual modality while exploits the
noisy aural modality using the inter-modality attention mechanism. To make full
use of the data and alleviate over-fitting, cross-validation is carried out on
the training and validation set. The concordance correlation coefficient (CCC)
centering is used to merge the results from each fold. On the test (validation)
set of the Aff-Wild2 database, the achieved CCC is 0.463 (0.469) for valence
and 0.492 (0.649) for arousal, which significantly outperforms the baseline
method with the corresponding CCC of 0.200 (0.210) and 0.190 (0.230) for
valence and arousal, respectively. The code is available at
https://github.com/sucv/ABAW2.
