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

在这个项目中，我们的目标是通过探索需要细粒度识别的问题来推动视频理解技术的边界。为此，我们将利用和开发新的深度学习方法，能够复杂地理解视频中的人类行为。这些方法将包括用递归网络对时间信息进行建模，开发注意机制，以有机地发现人类运动和表情的最具区分性的特征；以及处于当今视觉智能前沿的少镜头学习。我们期待这个项目将推动人类行为识别的最先进水平，这构成了许多机器人应用的基础。同时，我们期待我们在视频理解的深度学习方面取得的进展将成为计算机视觉的基础贡献，并与其他视频理解应用程序相关。

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.