论文标题
视频预期的深度序列学习：从离散和确定性到连续和随机性
Deep Sequence Learning for Video Anticipation: From Discrete and Deterministic to Continuous and Stochastic
论文作者
论文摘要
视频预期是预测有限的部分观察结果的一个/多个将来表示的任务。这是一项具有挑战性的任务，因为鉴于有限的观察，未来的代表性可能是高度模棱两可的。根据任务的性质，可以从两个观点考虑视频预期：细节的水平和预测未来的确定性水平。在这项研究中，我们从预期确定性未来的粗略表示，然后朝着预测随机过程的连续和细粒度的未来表示。前者的例子是视频动作的预期，我们有兴趣预测一个动作标签，并在某些观察到的视频中进行了预测，而后者的例子是预测人类运动的多种多样的连续性，给定部分观察到了一个。特别是，在本文中，我们为视频预期的文献做出了一些贡献...
Video anticipation is the task of predicting one/multiple future representation(s) given limited, partial observation. This is a challenging task due to the fact that given limited observation, the future representation can be highly ambiguous. Based on the nature of the task, video anticipation can be considered from two viewpoints: the level of details and the level of determinism in the predicted future. In this research, we start from anticipating a coarse representation of a deterministic future and then move towards predicting continuous and fine-grained future representations of a stochastic process. The example of the former is video action anticipation in which we are interested in predicting one action label given a partially observed video and the example of the latter is forecasting multiple diverse continuations of human motion given partially observed one. In particular, in this thesis, we make several contributions to the literature of video anticipation...
