Abstract
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.
Abstract (translated)
由于个人媒体的普及和媒体技术的进步，视频流量正以相当大的速度增长。因此，越来越需要自动分类运动图像的技术。本文使用NetVLAD和NetFV模型以及用于视频分类问题的Huber损失函数和用于验证实验的YouTube-8M数据集。我们根据数据集尝试了各种尝试并优化了超参数，最终获得了0.8668的GAP分数。
URL
https://arxiv.org/abs/1808.08671
