Composed Image Retrieval for Remote Sensing

• URL: http://arxiv.org/abs/2405.15587v3
• Date: Mon, 29 Jul 2024 01:10:15 GMT
• Title: Composed Image Retrieval for Remote Sensing
• Authors: Bill Psomas, Ioannis Kakogeorgiou, Nikos Efthymiadis, Giorgos Tolias, Ondrej Chum, Yannis Avrithis, Konstantinos Karantzalos,
• Abstract summary: 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. A novel method fusing image-to-image and text-to-image similarity is introduced.
• Score: 24.107610091033997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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