Video 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.
@article{arxiv.1808.08671,
title = {Approach for Video Classification with Multi-label on YouTube-8M Dataset},
author = {Kwangsoo Shin and Junhyeong Jeon and Seungbin Lee and Boyoung Lim and Minsoo Jeong and Jongho Nang},
journal= {arXiv preprint arXiv:1808.08671},
year = {2018}
}
Comments
Accepted at The 2nd Workshop on YouTube-8M Large-Scale Video Understanding in ECCV 2018