# The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge

arXiv: https://arxiv.org/abs/1706.05150 · HTML: https://mlsanity.com/paper/1706.05150/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2017.

## Built on by (most cited first, 2 of 2)

- [Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification](https://mlsanity.com/paper/2408.14441/) (2024)
- [Classifying Video based on Automatic Content Detection Overview](https://mlsanity.com/paper/2103.15323/) (2021)

## Abstract (arXiv)

This article describes the final solution of team monkeytyping, who finished in second place in the YouTube-8M video understanding challenge. The dataset used in this challenge is a large-scale benchmark for multi-label video classification. We extend the work in [1] and propose several improvements for frame sequence modeling. We propose a network structure called Chaining that can better capture the interactions between labels. Also, we report our approaches in dealing with multi-scale information and attention pooling. In addition, We find that using the output of model ensemble as a side target in training can boost single model performance. We report our experiments in bagging, boosting, cascade, and stacking, and propose a stacking algorithm called attention weighted stacking. Our final submission is an ensemble that consists of 74 sub models, all of which are listed in the appendix.