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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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