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- W4372270168 abstract "Hinted by a modest support set, few-shot action detection (FSAD) aims at localizing the action instances of unseen classes within an untrimmed query video. Existing FSAD techniques mostly rely on generating a set of class-agnostic action proposals from the query video and then finding the most plausible ones by assessing their correlation to the support set. Such two-stage approaches are feasible but not efficient, largely due to neglecting the support information in generating the proposals. This work focuses on the one-shot image scenario and introduces the attention zooming in strategy to effectively and progressively carry out support-query cross-attention while generating proposals. The resulting one-stage model yields high-quality action proposals for boosting one-shot action detection (OSAD) performance. Our extensive experiments on the ActivityNet-1.3 and THUMOS-14 datasets demonstrate that the proposed framework can achieve state-of-the-art performance in tack-ling challenging image-based OSAD tasks." @default.
- W4372270168 created "2023-05-07" @default.
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- W4372270168 date "2023-06-04" @default.
- W4372270168 modified "2023-10-05" @default.
- W4372270168 title "One-Shot Action Detection via Attention Zooming In" @default.
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- W4372270168 doi "https://doi.org/10.1109/icassp49357.2023.10095638" @default.
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