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We propose an audio-visual spatial-temporal deep neural network with: (1) avisual block containing a pretrained 2D-CNN followed by a temporalconvolutional network (TCN); (2) an aural block containing several parallelTCNs; and (3) a leader-follower attentive fusion block combining theaudio-visual information. The TCN with large history coverage enables our modelto 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., 36or 48). The fusion block emphasizes the visual modality while exploits thenoisy aural modality using the inter-modality attention mechanism. To make fulluse of the data and alleviate over-fitting, cross-validation is carried out onthe 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 valenceand 0.492 (0.649) for arousal, which significantly outperforms the baselinemethod with the corresponding CCC of 0.200 (0.210) and 0.190 (0.230) forvalence and arousal, respectively. The code is available athttps://github.com/sucv/ABAW2.

Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion