A Multiscale Dynamic Temporal Convolution Network For Continuous Dimensional Emotion Recognition.

Video-based dimensional emotion recognition, especially long sequence modeling, is a challenging task. This paper proposes a Multiscale Dynamic Temporal Convolution Network(MDTCN) for long sequence modeling of dimensional emotion recognition. Although Temporal Convolution Network(TCN) has performed well in sequence modeling, there are still some limitations that the feature scale of each layer is single and the receptive field of the upper layer is too large to capture the short-term dependence. Therefore, Dynamic Dilated Block(DDB) is constructed to better capture the temporal dependence by using dilated convolutions of different kernel size. In order to obtain both long-term and short-term temporal features, Multiscale TCN Structure is proposed. Extensive experiments are conducted on the AFFWILD2 and SEWA Dataset and the competitive result is shown compared to state-of-the-art methods.