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The goal of this paper is to recognize actions in video without the need forexamples. Different from traditional zero-shot approaches we do not demand thedesign and specification of attribute classifiers and class-to-attributemappings to allow for transfer from seen classes to unseen classes. Our keycontribution is objects2action, a semantic word embedding that is spanned by askip-gram model of thousands of object categories. Action labels are assignedto an object encoding of unseen video based on a convex combination of actionand object affinities. Our semantic embedding has three main characteristics toaccommodate for the specifics of actions. First, we propose a mechanism toexploit multiple-word descriptions of actions and objects. Second, weincorporate the automated selection of the most responsive objects per action.And finally, we demonstrate how to extend our zero-shot approach to thespatio-temporal localization of actions in video. Experiments on four actiondatasets demonstrate the potential of our approach.

Objects2action: Classifying and localizing actions without any video example