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.- Anthology ID:
- 2024.findings-acl.747
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12575–12584
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.747
- DOI:
- Cite (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.
- Cite (Informal):
- Diving Deep into the Motion Representation of Video-Text Models (Devaraj et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.747.pdf