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arXiv · 1510.06939

Objects2action: Classifying and localizing actions without any video example

Mihir Jain·Jan C. van Gemert·Thomas Mensink·Cees G. M. Snoek
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Abstract

The goal of this paper is to recognize actions in video without the need for examples. Different from traditional zero-shot approaches we do not demand the design and specification of attribute classifiers and class-to-attribute mappings to allow for transfer from seen classes to unseen classes. Our key contribution is objects2action, a semantic word embedding that is spanned by a skip-gram model of thousands of object categories. Action labels are assigned to an object encoding of unseen video based on a convex combination of action and object affinities. Our semantic embedding has three main characteristics to accommodate for the specifics of actions. First, we propose a mechanism to exploit multiple-word descriptions of actions and objects. Second, we incorporate the automated selection of the most responsive objects per action. And finally, we demonstrate how to extend our zero-shot approach to the spatio-temporal localization of actions in video. Experiments on four action datasets demonstrate the potential of our approach.

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Mihir Jain, Jan C. van Gemert, Thomas Mensink, Cees G. M. Snoek. 2015-10-23. Objects2action: Classifying and localizing actions without any video example. https://arxiv.org/abs/1510.06939

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