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Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00356140" target="_blank" >RIV/68407700:21730/21:00356140 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/CVPR46437.2021.00970" target="_blank" >https://doi.org/10.1109/CVPR46437.2021.00970</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CVPR46437.2021.00970" target="_blank" >10.1109/CVPR46437.2021.00970</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers

  • Original language description

    Our objective is language-based search of large-scale image and video datasets. For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attractive as retrieval scales and is efficient for billions of images using approximate nearest neighbour search. An alternative approach of using vision-text transformers with cross-attention gives considerable improvements in accuracy over the joint embeddings, but is often inapplicable in practice for large-scale retrieval given the cost of the cross-attention mechanisms required for each sample at test time. This work combines the best of both worlds. We make the following three contributions. First, we equip transformerbased models with a new fine-grained cross-attention architecture, providing significant improvements in retrieval accuracy whilst preserving scalability. Second, we introduce a generic approach for combining a Fast dual encoder model with our Slow but accurate transformer-based model via distillation and re-ranking. Finally, we validate our approach on the Flickr30K image dataset where we show an increase in inference speed by several orders of magnitude while having results competitive to the state of the art. We also extend our method to the video domain, improving the state of the art on the VATEX dataset.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

  • ISBN

    978-1-6654-4509-2

  • ISSN

    1063-6919

  • e-ISSN

    2575-7075

  • Number of pages

    11

  • Pages from-to

    9821-9831

  • Publisher name

    IEEE Computer Society

  • Place of publication

    USA

  • Event location

    Nashville

  • Event date

    Jun 20, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000742075007087