Projects per year
Abstract
Long videos contain many repeating actions, events and shots. These repetitions are frequently given identical captions, which makes it difficult to retrieve the exact desired clip using a text search. In this paper, we formulate the problem of unique captioning: Given multiple clips with the same caption, we generate a new caption for each clip that uniquely identifies it. We propose Captioning by Discriminative Prompting (CDP), which predicts a property that can separate identically captioned clips, and use it to generate unique captions. We introduce two benchmarks for unique captioning, based on egocentric footage and timeloop movies – where repeating actions are common. We demonstrate that captions generated by CDP improve text-to-video R@1 by 15% for egocentric videos and 10% in timeloop movies.
| Original language | English |
|---|---|
| Article number | 60 |
| Number of pages | 16 |
| Journal | International Journal of Computer Vision (IJCV) |
| Volume | 134 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 13 Jan 2026 |
Bibliographical note
Publisher Copyright:© The Author(s) 2026.
Projects
- 2 Finished
-
Visual AI - Full Programme Grant Extension
Damen, D. (Principal Investigator)
1/06/23 → 30/11/25
Project: Research
-
8459 EPSRC EP/T004991/1 UMPIRE - Dima Aldamen Fellowship
Damen, D. (Principal Investigator)
1/02/20 → 31/01/25
Project: Research