- Author
- Year
- 2015
- Title
- Objects2action: Classifying and localizing actions without any video example
- Event
- ICCV 2015: IEEE International Conference on Computer Vision
- Book/source title
- Proceedings: 2015 IEEE International Conference on Computer Vision: 11-18 December 2015, Santiago, Chile
- Pages (from-to)
- 4588-4596
- Publisher
- Los Alamitos, CA: IEEE Computer Society
- ISBN
- 9781467383905
- Document type
- Conference contribution
- Faculty
- Faculty of Science (FNWI)
- Institute
- Informatics Institute (IVI)
- 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.
- URL
- go to publisher's site
- Language
- English
- Permalink
- http://hdl.handle.net/11245/1.488889
- Downloads
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