  We took part in the YouTube-8M Video Understanding Challenge hosted on
Kaggle, and achieved the 10th place within less than one month's time. In this
paper, we present an extensive analysis and solution to the underlying
machine-learning problem based on frame-level data, where major challenges are
identified and corresponding preliminary methods are proposed. It's noteworthy
that, with merely the proposed strategies and uniformly-averaging multi-crop
ensemble was it sufficient for us to reach our ranking. We also report the
methods we believe to be promising but didn't have enough time to train to
convergence. We hope this paper could serve, to some extent, as a review and
guideline of the YouTube-8M multi-label video classification benchmark,
inspiring future attempts and research.
