Composed Image Retrieval for Remote Sensing
<|reference_start|>Composed Image Retrieval for Remote Sensing: 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<|reference_end|>
@article{psomas2024composed,
    title={Composed Image Retrieval for Remote Sensing},
    author={Bill Psomas, Ioannis Kakogeorgiou, Nikos Efthymiadis, Giorgos Tolias,
  Ondrej Chum, Yannis Avrithis, Konstantinos Karantzalos},
    journal={arXiv preprint arXiv:2405.15587},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15587},
    primaryClass={cs.CV}
}