End-to-End Video Classification with Knowledge Graphs

Understand

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.