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• W2736115865 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." @default.
• W2736115865 created "2017-07-21" @default.
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• W2736115865 date "2017-07-14" @default.
• W2736115865 modified "2023-09-23" @default.
• W2736115865 title "Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding." @default.
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