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This article describes the final solution of team monkeytyping, who finishedin second place in the YouTube-8M video understanding challenge. The datasetused in this challenge is a large-scale benchmark for multi-label videoclassification. We extend the work in [1] and propose several improvements forframe sequence modeling. We propose a network structure called Chaining thatcan better capture the interactions between labels. Also, we report ourapproaches in dealing with multi-scale information and attention pooling. Inaddition, We find that using the output of model ensemble as a side target intraining can boost single model performance. We report our experiments inbagging, boosting, cascade, and stacking, and propose a stacking algorithmcalled attention weighted stacking. Our final submission is an ensemble thatconsists of 74 sub models, all of which are listed in the appendix.

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