  The audio-video based emotion recognition aims to classify a given video into
basic emotions. In this paper, we describe our approaches in EmotiW 2019, which
mainly explores emotion features and feature fusion strategies for audio and
visual modality. For emotion features, we explore audio feature with both
speech-spectrogram and Log Mel-spectrogram and evaluate several facial features
with different CNN models and different emotion pretrained strategies. For
fusion strategies, we explore intra-modal and cross-modal fusion methods, such
as designing attention mechanisms to highlights important emotion feature,
exploring feature concatenation and factorized bilinear pooling (FBP) for
cross-modal feature fusion. With careful evaluation, we obtain 65.5% on the
AFEW validation set and 62.48% on the test set and rank third in the challenge.
