Abstract
Query expansion is a standard technique in image retrieval, which enriches the original query by capturing various features from relevant images and further aggregating these features to create an expanded query. In this work, we present a new framework, which is based on incorporating uncertainty estimation on top of a self attention mechanism during the expansion procedure. An uncertainty network provides added information on the images that are relevant to the query, in order to increase the expressiveness of the expanded query. Experimental results demonstrate that integrating uncertainty information into a transformer network can improve the performance in terms of mean Average Precision (mAP) on standard image retrieval datasets in comparison to existing methods. Moreover, our approach is the first one that incorporates uncertainty in aggregation of information in a query expansion procedure.
Original language | English |
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Title of host publication | Pattern Recognition and Artificial Intelligence - 3rd International Conference, ICPRAI 2022, Proceedings |
Editors | Mounîm El Yacoubi, Eric Granger, Pong Chi Yuen, Umapada Pal, Nicole Vincent |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 109-120 |
Number of pages | 12 |
ISBN (Print) | 9783031090363 |
DOIs | |
Publication status | Published - 2022 |
Event | 3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022 - Paris, France Duration: 1 Jun 2022 → 3 Jun 2022 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13363 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022 |
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Country/Territory | France |
City | Paris |
Period | 1/06/22 → 3/06/22 |
Bibliographical note
Publisher Copyright:© 2022, Springer Nature Switzerland AG.
Keywords
- Image retrieval
- Self attention
- Uncertainty