Matching video net: Memory-based embedding for video action recognition

Most of recent successful researches on action recognition are based on deep learning structures. Nonetheless, training deep neural networks is notorious for requiring huge amount of data. On the other hand, not enough data can lead to an overfitted model. In this work, we propose a novel model, matching video net (MVN), which can be trained with a small amount of data. In order to avoid the problem of overfitting, we use a non-parametric setup on top of parametric networks with external memories. An input clip of video is transformed into an embedding space and matched to the memorized samples in the embedding space. Then, the similarities between the input and the memorized data are measured to determine the nearest neighbors. We perform experiments in a supervised manner on action recognition datasets, achieving state-of-the-art results. Moreover, we applied our model to one-shot learning problems with a novel training strategy. Our model achieves surprisingly good results in predicting unseen action classes from only a few examples.