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        <title>Diving Deep into the Motion Representation of Video-Text Models</title>
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        <namePart type="given">Chinmaya</namePart>
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    <name type="personal">
        <namePart type="given">Yiannis</namePart>
        <namePart type="family">Aloimonos</namePart>
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            <namePart type="given">Andre</namePart>
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    <abstract>Videos are more informative than images becausethey capture the dynamics of the scene.By representing motion in videos, we can capturedynamic activities. In this work, we introduceGPT-4 generated motion descriptions thatcapture fine-grained motion descriptions of activitiesand apply them to three action datasets.We evaluated several video-text models on thetask of retrieval of motion descriptions. Wefound that they fall far behind human expertperformance on two action datasets, raisingthe question of whether video-text models understandmotion in videos. To address it, weintroduce a method of improving motion understandingin video-text models by utilizingmotion descriptions. This method proves tobe effective on two action datasets for the motiondescription retrieval task. The results drawattention to the need for quality captions involvingfine-grained motion information in existingdatasets and demonstrate the effectiveness ofthe proposed pipeline in understanding finegrainedmotion during video-text retrieval.</abstract>
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%0 Conference Proceedings
%T Diving Deep into the Motion Representation of Video-Text Models
%A Devaraj, Chinmaya
%A Fermuller, Cornelia
%A Aloimonos, Yiannis
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F devaraj-etal-2024-diving
%X Videos are more informative than images becausethey capture the dynamics of the scene.By representing motion in videos, we can capturedynamic activities. In this work, we introduceGPT-4 generated motion descriptions thatcapture fine-grained motion descriptions of activitiesand apply them to three action datasets.We evaluated several video-text models on thetask of retrieval of motion descriptions. Wefound that they fall far behind human expertperformance on two action datasets, raisingthe question of whether video-text models understandmotion in videos. To address it, weintroduce a method of improving motion understandingin video-text models by utilizingmotion descriptions. This method proves tobe effective on two action datasets for the motiondescription retrieval task. The results drawattention to the need for quality captions involvingfine-grained motion information in existingdatasets and demonstrate the effectiveness ofthe proposed pipeline in understanding finegrainedmotion during video-text retrieval.
%U https://aclanthology.org/2024.findings-acl.747
%P 12575-12584

Markdown (Informal)

[Diving Deep into the Motion Representation of Video-Text Models](https://aclanthology.org/2024.findings-acl.747) (Devaraj et al., Findings 2024)

• Diving Deep into the Motion Representation of Video-Text Models (Devaraj et al., Findings 2024)

ACL

• Chinmaya Devaraj, Cornelia Fermuller, and Yiannis Aloimonos. 2024. Diving Deep into the Motion Representation of Video-Text Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 12575–12584, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.