# The YouTube-8M Kaggle Competition: Challenges and Methods

arXiv: https://arxiv.org/abs/1706.09274 · HTML: https://mlsanity.com/paper/1706.09274/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [Gradient-Boosted Based Structured and Unstructured Learning](https://mlsanity.com/paper/2302.14299/) (2023)

## Abstract (arXiv)

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.