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
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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/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