Cited By
View all- Sener FSinghania DYao A(2020)Temporal Aggregate Representations for Long-Range Video UnderstandingComputer Vision – ECCV 202010.1007/978-3-030-58517-4_10(154-171)Online publication date: 23-Aug-2020
The 2nd YouTube-8M Large-Scale Video Understanding Challenge
Authors: 
Joonseok Lee, Apostol (Paul) Natsev, Walter Reade, Rahul Sukthankar, George TodericiAuthors Info & Claims
Joonseok Lee, Apostol (Paul) Natsev, Walter Reade, Rahul Sukthankar, George TodericiAuthors Info & ClaimsPages 193 - 205
Abstract
We hosted the 2nd YouTube-8M Large-Scale Video Understanding Kaggle Challenge and Workshop at ECCV’18, with the task of classifying videos from frame-level and video-level audio-visual features. In this year’s challenge, we restricted the final model size to 1 GB or less, encouraging participants to explore representation learning or better architecture, instead of heavy ensembles of multiple models. In this paper, we briefly introduce the YouTube-8M dataset and challenge task, followed by participants statistics and result analysis. We summarize proposed ideas by participants, including architectures, temporal aggregation methods, ensembling and distillation, data augmentation, and more.
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Published In
Sep 2018
729 pages
© Springer Nature Switzerland AG 2019.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 23 January 2019
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View all- Sener FSinghania DYao A(2020)Temporal Aggregate Representations for Long-Range Video UnderstandingComputer Vision – ECCV 202010.1007/978-3-030-58517-4_10(154-171)Online publication date: 23-Aug-2020
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