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        <title>Zero-Shot Activity Recognition with Verb Attribute Induction</title>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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    <abstract>In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb “salute” has several properties, such as being a light movement, a social act, and short in duration. We use these attributes as the internal mapping between visual and textual representations to reason about a previously unseen action. In contrast to much prior work that assumes access to gold standard attributes for zero-shot classes and focuses primarily on object attributes, our model uniquely learns to infer action attributes from dictionary definitions and distributed word representations. Experimental results confirm that action attributes inferred from language can provide a predictive signal for zero-shot prediction of previously unseen activities.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Activity Recognition with Verb Attribute Induction
%A Zellers, Rowan
%A Choi, Yejin
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F zellers-choi-2017-zero
%X In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb “salute” has several properties, such as being a light movement, a social act, and short in duration. We use these attributes as the internal mapping between visual and textual representations to reason about a previously unseen action. In contrast to much prior work that assumes access to gold standard attributes for zero-shot classes and focuses primarily on object attributes, our model uniquely learns to infer action attributes from dictionary definitions and distributed word representations. Experimental results confirm that action attributes inferred from language can provide a predictive signal for zero-shot prediction of previously unseen activities.
%R 10.18653/v1/D17-1099
%U https://aclanthology.org/D17-1099
%U https://doi.org/10.18653/v1/D17-1099
%P 946-958

Markdown (Informal)

[Zero-Shot Activity Recognition with Verb Attribute Induction](https://aclanthology.org/D17-1099) (Zellers & Choi, EMNLP 2017)

• Zero-Shot Activity Recognition with Verb Attribute Induction (Zellers & Choi, EMNLP 2017)

ACL

• Rowan Zellers and Yejin Choi. 2017. Zero-Shot Activity Recognition with Verb Attribute Induction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 946–958, Copenhagen, Denmark. Association for Computational Linguistics.