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.

利用 NetVLAD 和 NetFV 模型，使用 Huber 损失函数和 YouTube-8M 数据集实现视频分类问题，采用各种技术和优化方法，最终得到 GAP 得分 0.8668。

在 YouTube-8M 数据集上进行多标签视频分类的方法

Approach for Video Classification with Multi-label on YouTube-8M Dataset

Video classification problem has been studied many years. The success of
Convolutional Neural Networks (CNN) in image recognition tasks gives a powerful
incentive for researchers to create more advanced video classification
approaches. As video has a temporal content Long Short Term Memory (LSTM)
networks become handy tool allowing to model long-term temporal clues. Both
approaches need a large dataset of input data. In this paper three models
provided to address video classification using recently announced YouTube-8M
large-scale dataset. The first model is based on frame pooling approach. Two
other models based on LSTM networks. Mixture of Experts intermediate layer is
used in third model allowing to increase model capacity without dramatically
increasing computations. The set of experiments for handling imbalanced
training data has been conducted.

本文基于 YouTube-8M 大规模数据集，提出了三种视频分类模型，分别基于帧池化和 LSTM 网络，第三个模型使用 Experts 混合中间层以增加模型容量，并进行了一系列处理不平衡训练数据的实验。