Sequential Interactive Biased Network for Context-Aware Emotion Recognition

Emotion context information is crucial yet complicated for emotion recognition. How to process it is a challenging problem. Existing works mainly extract context representations of the face, body and scene independently. These strategies may be limited in the understanding of emotional context relation. To address this problem, we propose Sequential Interactive Biased Network (SIB-Net), which is motivated by the studies that the context contains sequential, interactive and biased relation. Specifically, SIB-Net captures and utilizes the context relation by three modules: i) a Sequential Context Module captures consecutive relation with a GRU-like architecture, ii) an Interactive Context Module acquires cooperative context with global correlated linear fusion, and iii) a Biased Context Module benefits from the biased relation with distribution labels and the L1 loss. Extensive experiments on EMOTIC and CAER datasets show that our SIB-Net improves baseline significantly and achieves comparable results to the state-of-the-art methods.