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Approach for Video Classification with Multi-label on YouTube-8M Dataset
AbstractVideo traffic is increasing at a considerable rate due to the spread of personal media and advancements in media technology. Accordingly, there is a growing need for techniques to automatically classify moving images. This paper use NetVLAD and NetFV models and the Huber loss function for video classification problem and YouTube-8M dataset to verify the experiment. We tried various attempts according to the dataset and optimize hyperparameters, ultimately obtain a GAP score of 0.8668.
Related Material
[pdf] [arXiv][bibtex]@InProceedings{Shin_2018_ECCV_Workshops,
author = {Shin, Kwangsoo and Jeon, Junhyeong and Lee, Seungbin and Lim, Boyoung and Jeong, Minsoo and Nang, Jongho},
title = {Approach for Video Classification with Multi-label on YouTube-8M Dataset},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}
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