Papers › Objects2action: Classifying and localizing actions without any video example
Objects2action: Classifying and localizing actions without any video example
Mihir Jain, Jan C. van Gemert, Thomas Mensink, Cees G. M. Snoek
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Action Recognition | HMDB51 | O2A | Top-1 Accuracy | 15.6 | #27 of 29 | Archive leaderboard | report |
| Zero-Shot Action Recognition | UCF101 | O2A | Top-1 Accuracy | 30.3 | #24 of 35 | Archive leaderboard | report |
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