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Abstract
Video Moment Retrieval is a common task to evaluate the performance of visual-language models-it involves localising start and end times of moments in videos from query sentences. The current task formulation assumes that the queried moment is present in the video, resulting in false positive moment predictions when irrelevant query sentences are provided. In this paper we propose the task of Negative-Aware Video Moment Retrieval (NA-VMR), which considers both moment retrieval accuracy and negative query rejection accuracy. We make the distinction between In-Domain and Out-of-Domain negative queries and provide new evaluation benchmarks for two popular video moment retrieval datasets: QVHighlights and Charades-STA. We analyse the ability of current SOTA video moment retrieval approaches to adapt to Negative-Aware Video Moment Retrieval and propose UniVTG-NA, an adaptation of UniVTG designed to tackle NA-VMR. UniVTG-NA achieves high negative rejection accuracy (avg. 98.4%) scores while retaining moment retrieval scores to within 3.87% Recall@1. Dataset splits are available at https://github.com/keflanagan/MomentofUntruth.
Original language | English |
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Title of host publication | 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 5336-5345 |
Number of pages | 10 |
ISBN (Electronic) | 9798331510831 |
ISBN (Print) | 9798331510848 |
DOIs | |
Publication status | Published - 8 Apr 2025 |
Event | IEEE/CVF Winter Conference on Applications of Computer Vision: WACV - Tuscon, Arizona, United States Duration: 28 Feb 2025 → 5 Mar 2025 https://wacv2025.thecvf.com/ |
Publication series
Name | IEEE Workshop on Applications of Computer Vision (WACV) |
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Publisher | IEEE |
ISSN (Print) | 2472-6737 |
ISSN (Electronic) | 2642-9381 |
Conference
Conference | IEEE/CVF Winter Conference on Applications of Computer Vision |
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Country/Territory | United States |
City | Tuscon, Arizona |
Period | 28/02/25 → 5/03/25 |
Internet address |
Bibliographical note
Publisher Copyright:© 2025 IEEE.
Fingerprint
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8030 EPSRC via Oxford EP/T028572/1 Visual AI
Damen, D. (Principal Investigator)
1/12/20 → 30/11/25
Project: Research, Parent
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UMPIRE: United Model for the Perception of Interactions for visual Recognition
Damen, D. (Principal Investigator)
1/02/20 → 31/01/25
Project: Research