- Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
- 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)
- Piscataway, NJ: IEEE
- ISBN (electronic)
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
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.
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- Accepted author manuscript
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