Retrieving and Highlighting Action with Spatiotemporal Reference

• URL: http://arxiv.org/abs/2005.09183v1
• Date: Tue, 19 May 2020 03:12:31 GMT
• Title: Retrieving and Highlighting Action with Spatiotemporal Reference
• Authors: Seito Kasai, Yuchi Ishikawa, Masaki Hayashi, Yoshimitsu Aoki, Kensho Hara, Hirokatsu Kataoka
• Abstract summary: We present a framework that jointly retrieves andtemporally highlights actions in videos. Our work takes on the novel task of highlighting action highlighting, which visualizes where and when actions occur in an un video setting.
• Score: 15.283548146322971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods. Our work takes on the novel task of action highlighting, which visualizes where and when actions occur in an untrimmed video setting. Action highlighting is a fine-grained task, compared to conventional action recognition tasks which focus on classification or window-based localization. Leveraging weak supervision from annotated captions, our framework acquires spatiotemporal relevance maps and generates local embeddings which relate to the nouns and verbs in captions. Through experiments, we show that our model generates various maps conditioned on different actions, in which conventional visual reasoning methods only go as far as to show a single deterministic saliency map. Also, our model improves retrieval recall over our baseline without alignment by 2-3% on the MSR-VTT dataset.

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