Applications of Autoencoders part4(Artificial Intelligence )

Abstract: Recent studies utilizing multi-modal data aimed at building a robust model for facial Action Unit (AU) detection. However, due to the heterogeneity of multi-modal data, multi-modal representation learning becomes one of the main challenges. On one hand, it is difficult to extract the relevant features from multi-modalities by only one feature extractor, on the other hand, previous studies have not fully explored the potential of multi-modal fusion strategies. For example, early fusion usually required all modalities to be present during inference, while late fusion and middle fusion increased the network size for feature learning. In contrast to a large amount of work on late fusion, there are few works on early fusion to explore the channel information.