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.

本文描述了 team monkeytyping 的最终方案，该方案在YouTube-8M视频理解挑战中获得第二名。作者在帧序列建模方面提出了几个改进，包括更好地捕捉标签之间的交互和使用模型集成来提高单个模型性能等。此外，作者还提出了一种名为 attention weighted stacking 的堆叠算法。最终提交的集成包含74个子模型。

YouTube-8M视频理解挑战的猴子打字解决方案