AMES: Asymmetric and Memory-Efficient Similarity Estimation for Instance-Level Retrieval

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00378907" target="_blank" >RIV/68407700:21230/25:00378907 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-73202-7_18" target="_blank" >https://doi.org/10.1007/978-3-031-73202-7_18</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-73202-7_18" target="_blank" >10.1007/978-3-031-73202-7_18</a>

Result language

angličtina

Original language name

AMES: Asymmetric and Memory-Efficient Similarity Estimation for Instance-Level Retrieval

Original language description

This work investigates the problem of instance-level image retrieval re-ranking with the constraint of memory efficiency, ultimately aiming to limit memory usage to 1KB per image. Departing from the prevalent focus on performance enhancements, this work prioritizes the crucial trade-off between performance and memory requirements. The proposed model uses a transformer-based architecture designed to estimate image-to-image similarity by capturing interactions within and across images based on their local descriptors. A distinctive property of the model is the capability for asymmetric similarity estimation. Database images are represented with a smaller number of descriptors compared to query images, enabling performance improvements without increasing memory consumption. To ensure adaptability across different applications, a universal model is introduced that adjusts to a varying number of local descriptors during the testing phase. Results on standard benchmarks demonstrate the superiority of our approach over both hand-crafted and learned models. In particular, compared with current state-of-the-art methods that overlook their memory footprint, our approach not only attains superior performance but does so with a significantly reduced memory footprint. The code and pretrained models are publicly available at: https://github.com/pavelsuma/ames

Czech name

—

Czech description

—

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

Computer Vision – ECCV 2024, Part LIX

ISBN

978-3-031-73201-0

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

19

Pages from-to

307-325

Publisher name

Springer, Cham

Place of publication

—

Event location

Milano

Event date

Sep 29, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001401048900018