Composed Image Retrieval for Remote Sensing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00379044" target="_blank" >RIV/68407700:21230/24:00379044 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/IGARSS53475.2024.10642874" target="_blank" >https://doi.org/10.1109/IGARSS53475.2024.10642874</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IGARSS53475.2024.10642874" target="_blank" >10.1109/IGARSS53475.2024.10642874</a>

Result language

angličtina

Original language name

Composed Image Retrieval for Remote Sensing

Original language description

This work introduces composed image retrieval to remote sensing. It allows to query a large image archive by image examples alternated by a textual description, enriching the descriptive power over unimodal queries, either visual or textual. Various attributes can be modified by the textual part, such as shape, color, or context. A novel method fusing image-to-image and text-to-image similarity is introduced. We demonstrate that a vision-language model possesses sufficient descriptive power and no further learning step or training data are necessary. We present a new evaluation benchmark focused on color, context, density, existence, quantity, and shape modifications. Our work not only sets the state-of-the-art for this task, but also serves as a foundational step in addressing a gap in the field of remote sensing image retrieval. Code at: https://github.com/billpsomas/rscir.

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

2024

Confidentiality

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

Article name in the collection

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium Proceedings

ISBN

979-8-3503-6033-2

ISSN

2153-6996

e-ISSN

2153-7003

Number of pages

9

Pages from-to

8526-8534

Publisher name

IEEE

Place of publication

Piscataway

Event location

Athens

Event date

Jul 7, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001415226903077