Large-Scale YouTube-8M Video Understanding with Deep Neural Networks
This work addresses video classification for researchers, but it is incremental as it applies existing methods to a new dataset.
The paper tackled video classification using the YouTube-8M dataset, proposing three models including frame pooling and LSTM-based approaches, with a Mixture of Experts layer to boost capacity efficiently.
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.