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arXiv · 1605.03324

Unsupervised Semantic Action Discovery from Video Collections

Ozan Sener·Amir Roshan Zamir·Chenxia Wu·Silvio Savarese·Ashutosh Saxena
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Abstract

Human communication takes many forms, including speech, text and instructional videos. It typically has an underlying structure, with a starting point, ending, and certain objective steps between them. In this paper, we consider instructional videos where there are tens of millions of them on the Internet. We propose a method for parsing a video into such semantic steps in an unsupervised way. Our method is capable of providing a semantic "storyline" of the video composed of its objective steps. We accomplish this using both visual and language cues in a joint generative model. Our method can also provide a textual description for each of the identified semantic steps and video segments. We evaluate our method on a large number of complex YouTube videos and show that our method discovers semantically correct instructions for a variety of tasks.

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BibTeXRIS

Ozan Sener, Amir Roshan Zamir, Chenxia Wu, Silvio Savarese, Ashutosh Saxena. 2016-05-11. Unsupervised Semantic Action Discovery from Video Collections. https://arxiv.org/abs/1605.03324

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