Leveraging LLMs and Generative Models for Interactive Known-Item Video Search
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Title of host publication | MultiMedia Modeling |
Subtitle of host publication | 30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29 – February 2, 2024, Proceedings, Part IV |
Editors | Stevan Rudinac, Alan Hanjalic, Cynthia Liem, Marcel Worring, Björn Þór Jónsson, Bei Liu, Yoko Yamakata |
Place of Publication | Cham |
Publisher | Springer |
Pages | 380-386 |
ISBN (electronic) | 978-3-031-53302-0 |
ISBN (print) | 978-3-031-53301-3 |
Publication status | Published - 2024 |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Volume | 14557 |
ISSN (Print) | 0302-9743 |
ISSN (electronic) | 1611-3349 |
Conference
Title | 30th International Conference on MultiMedia Modeling (MMM 2024) |
---|---|
Place | Netherlands |
City | Amsterdam |
Period | 29 January - 2 February 2024 |
Link(s)
Abstract
While embedding techniques such as CLIP have considerably boosted search performance, user strategies in interactive video search still largely operate on a trial-and-error basis. Users are often required to manually adjust their queries and carefully inspect the search results, which greatly rely on the users’ capability and proficiency. Recent advancements in large language models (LLMs) and generative models offer promising avenues for enhancing interactivity in video retrieval and reducing the personal bias in query interpretation, particularly in the known-item search. Specifically, LLMs can expand and diversify the semantics of the queries while avoiding grammar mistakes or the language barrier. In addition, generative models have the ability to imagine or visualize the verbose query as images. We integrate these new LLM capabilities into our existing system and evaluate their effectiveness on V3C1 and V3C2 datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Research Area(s)
- Generative Model, Interactive Video Retrieval, Known-Item Search, Large Language Models
Citation Format(s)
Leveraging LLMs and Generative Models for Interactive Known-Item Video Search. / Ma, Zhixin; Wu, Jiaxin; Ngo, Chong Wah.
MultiMedia Modeling: 30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29 – February 2, 2024, Proceedings, Part IV. ed. / Stevan Rudinac; Alan Hanjalic; Cynthia Liem; Marcel Worring; Björn Þór Jónsson; Bei Liu; Yoko Yamakata. Cham: Springer, 2024. p. 380-386 (Lecture Notes in Computer Science; Vol. 14557).
MultiMedia Modeling: 30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29 – February 2, 2024, Proceedings, Part IV. ed. / Stevan Rudinac; Alan Hanjalic; Cynthia Liem; Marcel Worring; Björn Þór Jónsson; Bei Liu; Yoko Yamakata. Cham: Springer, 2024. p. 380-386 (Lecture Notes in Computer Science; Vol. 14557).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review