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.
@article{arxiv.1605.03324,
title = {Unsupervised Semantic Action Discovery from Video Collections},
author = {Ozan Sener and Amir Roshan Zamir and Chenxia Wu and Silvio Savarese and Ashutosh Saxena},
journal= {arXiv preprint arXiv:1605.03324},
year = {2016}
}
Comments
First version of this paper arXiv:1506.08438 appeared in ICCV 2015. This extended version has more details on the learning algorithm and hierarchical clustering with full derivation, additional analysis on the robustness to the subtitle noise, and a novel application on robotics