VClipper: Exploiting CLIP Zero-shot capabilities for moment retrieval in video recordings
This research explores the integration of CLIP, a pretrained model, into video content analysis. In a landscape inundated with multimedia data, pinpointing specific moments within videos is a persistent challenge. By leveraging CLIP's semantic and visual search capabilities, this study endeavors to...
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| Format: | Master's thesis |
| Publication date: | 2024 |
| Country: | España |
| Institution: | Universitat Oberta de Catalunya (UOC) |
| Repository: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/149808 |
| Online Access: | http://hdl.handle.net/10609/149808 |
| Access Level: | Open access |
| Keyword: | video analysis moment retrieval CLIP Computer vision -- TFM Visió per ordinador -- TFM |
| Summary: | This research explores the integration of CLIP, a pretrained model, into video content analysis. In a landscape inundated with multimedia data, pinpointing specific moments within videos is a persistent challenge. By leveraging CLIP's semantic and visual search capabilities, this study endeavors to refine content retrieval methods. Emphasizing efficiency and applicability, this study aims to make this process more precise and practical. With this research we also reviewed the state-of-the-art methods and produced empirical analysis on the effects of postprocessing on the similarity vectors obtained from CLIP encoders. Finally, we developed two distinct methods aimed at moment retrieval tasks in audiovisual data, obtaining a model that is able to outperform previous works in Zero-shot moment revival, reaching 57.3 at R@1 IoU=0.5 and 51.6 at mAP@0.5. |
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