  Leveraging both visual frames and audio has been experimentally proven
effective to improve large-scale video classification. Previous research on
video classification mainly focuses on the analysis of visual content among
extracted video frames and their temporal feature aggregation. In contrast,
multimodal data fusion is achieved by simple operators like average and
concatenation. Inspired by the success of bilinear pooling in the visual and
language fusion, we introduce multi-modal factorized bilinear pooling (MFB) to
fuse visual and audio representations. We combine MFB with different
video-level features and explore its effectiveness in video classification.
Experimental results on the challenging Youtube-8M v2 dataset demonstrate that
MFB significantly outperforms simple fusion methods in large-scale video
classification.
