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arXiv · 1707.04555

Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding

Fu Li·Chuang Gan·Xiao Liu·Yunlong Bian·Xiang Long·Yandong Li·Zhichao Li·Jie Zhou·Shilei Wen
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Abstract

This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place. Because the challenge provides pre-extracted visual and audio features instead of the raw videos, we mainly investigate various temporal modeling approaches to aggregate the frame-level features for multi-label video recognition. Our system contains three major components: two-stream sequence model, fast-forward sequence model and temporal residual neural networks. Experiment results on the challenging Youtube-8M dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing temporal modeling approaches in the large-scale video recognition tasks. To be noted, our fast-forward LSTM with a depth of 7 layers achieves 82.75% in term of GAP@20 on the Kaggle Public test set.

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Fu Li, Chuang Gan, Xiao Liu, Yunlong Bian, Xiang Long, Yandong Li, Zhichao Li, Jie Zhou, Shilei Wen. 2017-07-14. Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding. https://arxiv.org/abs/1707.04555

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