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        <title>Natural Language Descriptions for Human Activities in Video Streams</title>
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            <title>Proceedings of the 10th International Conference on Natural Language Generation</title>
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            <namePart type="given">Jose</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Alonso</namePart>
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        <name type="personal">
            <namePart type="given">Alberto</namePart>
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            <namePart type="given">Ehud</namePart>
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    <abstract>There has been continuous growth in the volume and ubiquity of video material. It has become essential to define video semantics in order to aid the searchability and retrieval of this data. We present a framework that produces textual descriptions of video, based on the visual semantic content. Detected action classes rendered as verbs, participant objects converted to noun phrases, visual properties of detected objects rendered as adjectives and spatial relations between objects rendered as prepositions. Further, in cases of zero-shot action recognition, a language model is used to infer a missing verb, aided by the detection of objects and scene settings. These extracted features are converted into textual descriptions using a template-based approach. The proposed video descriptions framework evaluated on the NLDHA dataset using ROUGE scores and human judgment evaluation.</abstract>
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%0 Conference Proceedings
%T Natural Language Descriptions for Human Activities in Video Streams
%A Alharbi, Nouf
%A Gotoh, Yoshihiko
%Y Alonso, Jose M.
%Y Bugarín, Alberto
%Y Reiter, Ehud
%S Proceedings of the 10th International Conference on Natural Language Generation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Santiago de Compostela, Spain
%F alharbi-gotoh-2017-natural
%X There has been continuous growth in the volume and ubiquity of video material. It has become essential to define video semantics in order to aid the searchability and retrieval of this data. We present a framework that produces textual descriptions of video, based on the visual semantic content. Detected action classes rendered as verbs, participant objects converted to noun phrases, visual properties of detected objects rendered as adjectives and spatial relations between objects rendered as prepositions. Further, in cases of zero-shot action recognition, a language model is used to infer a missing verb, aided by the detection of objects and scene settings. These extracted features are converted into textual descriptions using a template-based approach. The proposed video descriptions framework evaluated on the NLDHA dataset using ROUGE scores and human judgment evaluation.
%R 10.18653/v1/W17-3512
%U https://aclanthology.org/W17-3512
%U https://doi.org/10.18653/v1/W17-3512
%P 85-94

Markdown (Informal)

[Natural Language Descriptions for Human Activities in Video Streams](https://aclanthology.org/W17-3512) (Alharbi & Gotoh, INLG 2017)

• Natural Language Descriptions for Human Activities in Video Streams (Alharbi & Gotoh, INLG 2017)

ACL

• Nouf Alharbi and Yoshihiko Gotoh. 2017. Natural Language Descriptions for Human Activities in Video Streams. In Proceedings of the 10th International Conference on Natural Language Generation, pages 85–94, Santiago de Compostela, Spain. Association for Computational Linguistics.