Two-Stream Convolutional Networks for Action Recognition in Videos

We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion between frames. We also aim to generalise the best performing hand-crafted features within a data-driven learning framework. Our contribution is three-fold. First, we propose a two-stream ConvNet architecture which incorporates spatial and temporal networks. Second, we demonstrate that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data. Finally, we show that multi-task learning, applied to two different action classification datasets, can be used to increase the amount of training data and improve the performance on both. Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art. It also exceeds by a large margin previous attempts to use deep nets for video classification

Deep learning for supervised classification

One of the most recent area in the Machine Learning research is Deep Learning. Deep Learning algorithms have been applied successfully to computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics. The key idea of Deep Learning is to combine the best techniques from Machine Learning to build powerful general‑purpose learning algorithms. It is a mistake to identify Deep Neural Networks with Deep Learning Algorithms. Other approaches are possible, and in this paper we illustrate a generalization of Stacking which has very competitive performances. In particular, we show an application of this approach to a real classification problem, where a three-stages Stacking has proved to be very effective