Large-scale Video Classification guided by Batch Normalized LSTM Translator
This work addresses the challenging problem of multi-label classification for large video datasets, which is incremental as it builds on existing LSTM and batch normalization techniques.
The authors tackled large-scale multi-label video classification on the YouTube-8M dataset by proposing a novel method that treats labels as words and uses deep recurrent neural networks with LSTMs, batch normalization, and stochastic gating, reporting improved validation results.
Youtube-8M dataset enhances the development of large-scale video recognition technology as ImageNet dataset has encouraged image classification, recognition and detection of artificial intelligence fields. For this large video dataset, it is a challenging task to classify a huge amount of multi-labels. By change of perspective, we propose a novel method by regarding labels as words. In details, we describe online learning approaches to multi-label video classification that are guided by deep recurrent neural networks for video to sentence translator. We designed the translator based on LSTMs and found out that a stochastic gating before the input of each LSTM cell can help us to design the structural details. In addition, we adopted batch normalizations into our models to improve our LSTM models. Since our models are feature extractors, they can be used with other classifiers. Finally we report improved validation results of our models on large-scale Youtube-8M datasets and discussions for the further improvement.