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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 pp 567–587Cite as

Text-Based Temporal Localization of Novel Events

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13674))

Abstract

Recent works on text-based localization of moments have shown high accuracy on several benchmark datasets. However, these approaches are trained and evaluated relying on the assumption that the localization system, during testing, will only encounter events that are available in the training set (i.e., seen events). As a result, these models are optimized for a fixed set of seen events and they are unlikely to generalize to the practical requirement of localizing a wider range of events, some of which may be unseen. Moreover, acquiring videos and text comprising all possible scenarios for training is not practical. In this regard, this paper introduces and tackles the problem of text-based temporal localization of novel/unseen events. Our goal is to temporally localize video moments based on text queries, where both the video moments and text queries are not observed/available during training. Towards solving this problem, we formulate the inference task of text-based localization of moments as a relational prediction problem, hypothesizing a conceptual relation between semantically relevant moments, e.g., a temporally relevant moment corresponding to an unseen text query and a moment corresponding to a seen text query may contain shared concepts. The likelihood of a candidate moment to be the correct one based on an unseen text query will depend on its relevance to the moment corresponding to the semantically most relevant seen query. Empirical results on two text-based moment localization datasets show that our proposed approach can reach up to 15% absolute improvement in performance compared to existing localization approaches.

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Acknowledgment

This work was partially supported by ONR grant N00014-19-1-2264 and NSF grant 1901379.

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Authors and Affiliations

  1. University of California, Riverside, CA, USA

    Sudipta Paul & Amit K. Roy-Chowdhury

  2. SRI International, Princeton, NJ, USA

    Niluthpol Chowdhury Mithun

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  1. Sudipta Paul
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  2. Niluthpol Chowdhury Mithun
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  3. Amit K. Roy-Chowdhury
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Correspondence to Sudipta Paul .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Paul, S., Mithun, N.C., Roy-Chowdhury, A.K. (2022). Text-Based Temporal Localization of Novel Events. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_33

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