- Author
- Year
- 2017
- Title
- Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
- Event
- 2017 IEEE Conference on Computer Vision and Pattern Recognition
- Book/source title
- 30th IEEE Conference on Computer Vision and Pattern Recognition
- Book/source subtitle
- CVPR 2017 : 21-26 July 2016, Honolulu, Hawaii : proceedings
- Pages (from-to)
- 2087-2096
- Publisher
- Piscataway, NJ: IEEE
- ISBN
- 9781538604588
- ISBN (electronic)
- 9781538604571
- Document type
- Conference contribution
- Faculty
- Faculty of Science (FNWI)
- Institute
- Informatics Institute (IVI)
- Abstract
-
Event detection in unconstrained videos is conceived as a content-based video retrieval with two modalities: textual and visual. Given a text describing a novel event, the goal is to rank related videos accordingly. This task is zero-exemplar, no video examples are given to the novel event.
Related works train a bank of concept detectors on external data sources. These detectors predict confidence scores for test videos, which are ranked and retrieved accordingly. In contrast, we learn a joint space in which the visual and textual representations are embedded. The space casts a novel event as a probability of pre-defined events. Also, it learns to measure the distance between an event and its related videos.
Our model is trained end-to-end on publicly available EventNet. When applied to TRECVID Multimedia Event Detection dataset, it outperforms the state-of-the-art by a considerable margin. - URL
- go to publisher's site
- Link
- Accepted author manuscript
- Other links
- Other link
- Language
- English
- Persistent Identifier
- https://hdl.handle.net/11245.1/d13b8e7c-155e-4ef6-92ac-6b74f585acff
- Downloads
-
Hussein_Unified_Embedding_and_CVPR_2017_paper(Accepted author manuscript)
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