The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge
This work addresses multi-label video classification for researchers, but it is incremental as it builds on prior methods.
The team tackled the YouTube-8M video understanding challenge by proposing improvements in frame sequence modeling, label interactions, and ensemble methods, achieving second place in the competition.
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.