Perceiving Humans in Detail: Fine-grained classification of Human Action Recognition

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In this project our goal is to push the boundaries of video understanding technology, by exploring the problems that require fine-grained recognition. For this we will leverage and develop novel deep learning methods, capable of sophisticated understanding of human action in videos. These methods will include modelling temporal information with recurrent networks, developing mechanisms of attention, to organically discover the most discriminative features of human motions and expressions; and few-shot learning, which is at the forefront of vision intelligence today. We expect that this project will advance the state-of-the-art of human action recognition which constitutes the basis of many robotics applications. At the same time, we expect the advances we make in deep learning for video understanding to be fundamental contributions to computer vision and relevant to other video understanding applications.