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Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00370772" target="_blank" >RIV/68407700:21230/23:00370772 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/ICCV51070.2023.01037" target="_blank" >https://doi.org/10.1109/ICCV51070.2023.01037</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICCV51070.2023.01037" target="_blank" >10.1109/ICCV51070.2023.01037</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations

  • Original language description

    Fine-grained and instance-level recognition methods are commonly trained and evaluated on specific domains, in a model per domain scenario. Such an approach, however, is impractical in real large-scale applications. In this work, we address the problem of universal image embedding, where a single universal model is trained and used in multiple domains. First, we leverage existing domain-specific datasets to carefully construct a new large-scale public benchmark for the evaluation of universal image embeddings, with 241k query images, 1.4M index images and 2.8M training images across 8 different domains and 349k classes. We define suitable metrics, training and evaluation protocols to foster future research in this area. Second, we provide a comprehensive experimental evaluation on the new dataset, demonstrating that existing approaches and simplistic extensions lead to worse performance than an assembly of models trained for each domain separately. Finally, we conducted a public research competition on this topic, leveraging industrial datasets, which attracted the participation of more than 1k teams world-wide. This exercise generated many interesting research ideas and findings which we present in detail. Project webpage: https://cmp.felk.cvut.cz/univ_emb/

  • 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/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    ICCV2023: Proceedings of the International Conference on Computer Vision

  • ISBN

    979-8-3503-0719-1

  • ISSN

    1550-5499

  • e-ISSN

    2380-7504

  • Number of pages

    12

  • Pages from-to

    11256-11267

  • Publisher name

    IEEE

  • Place of publication

    Piscataway

  • Event location

    Paris

  • Event date

    Oct 2, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    001169499003067