LOCORE: Image Re-ranking with Long-Context Sequence Modeling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00387292" target="_blank" >RIV/68407700:21230/25:00387292 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/CVPR52734.2025.00895" target="_blank" >https://doi.org/10.1109/CVPR52734.2025.00895</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR52734.2025.00895" target="_blank" >10.1109/CVPR52734.2025.00895</a>

Result language

angličtina

Original language name

LOCORE: Image Re-ranking with Long-Context Sequence Modeling

Original language description

We introduce LOCORE, Long-Context Re-ranker, a model that takes as input local descriptors corresponding to an image query and a list of gallery images and outputs similarity scores between the query and each gallery image. This model is used for image retrieval, where typically a first ranking is performed with an efficient similarity measure, and then a shortlist of top-ranked images is re-ranked based on a more fine-grained similarity model. Compared to existing methods that perform pair-wise similarity estimation with local descriptors or list-wise re-ranking with global descriptors, LOCORE is the first method to perform list-wise re-ranking with local descriptors. To achieve this, we leverage efficient long-context sequence models to effectively capture the dependencies between query and gallery images at the local-descriptor level. During testing, we process long shortlists with a sliding window strategy that is tailored to overcome the context size limitations of sequence models. Our approach achieves superior performance compared with other re-rankers on established image retrieval benchmarks of landmarks (ROxf and RPar), products (SOP), fashion items (In-Shop), and bird species (CUB-200) while having comparable latency to the pair-wise local descriptor re-rankers.

Czech name

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Czech description

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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)

Project

<a href="/en/project/GM21-28830M" target="_blank" >GM21-28830M: Learning Universal Visual Representation with Limited Supervision</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Article name in the collection

2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

ISBN

979-8-3315-4364-8

ISSN

1063-6919

e-ISSN

2575-7075

Number of pages

11

Pages from-to

9580-9590

Publisher name

IEEE Computer Society

Place of publication

Los Alamitos

Event location

Nashville

Event date

Jun 11, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001601106700312