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arXiv · 1705.02148

Unified Embedding and Metric Learning for Zero-Exemplar Event Detection

Noureldien Hussein·Efstratios Gavves·Arnold W. M. Smeulders
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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.

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BibTeXRIS

Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders. 2017-05-05. Unified Embedding and Metric Learning for Zero-Exemplar Event Detection. https://arxiv.org/abs/1705.02148

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