  Video understanding has attracted much research attention especially since
the recent availability of large-scale video benchmarks. In this paper, we
address the problem of multi-label video classification. We first observe that
there exists a significant knowledge gap between how machines and humans learn.
That is, while current machine learning approaches including deep neural
networks largely focus on the representations of the given data, humans often
look beyond the data at hand and leverage external knowledge to make better
decisions. Towards narrowing the gap, we propose to incorporate external
knowledge graphs into video classification. In particular, we unify traditional
"knowledgeless" machine learning models and knowledge graphs in a novel
end-to-end framework. The framework is flexible to work with most existing
video classification algorithms including state-of-the-art deep models.
Finally, we conduct extensive experiments on the largest public video dataset
YouTube-8M. The results are promising across the board, improving mean average
precision by up to 2.9%.
