{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/composed-image-retrieval-for-remote-sensing","title":"Composed Image Retrieval for Remote Sensing","arxiv_id":"2405.15587","date":"2024-05-24","proceeding":null,"authors":["Bill Psomas","Ioannis Kakogeorgiou","Nikos Efthymiadis","Giorgos Tolias","Ondrej Chum","Yannis Avrithis","Konstantinos Karantzalos"],"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","url_abs":"https://arxiv.org/abs/2405.15587v3","url_pdf":"https://arxiv.org/pdf/2405.15587v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"composed-image-retrieval-for-remote-sensing","repo_url":"https://github.com/billpsomas/rscir","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"composed-image-retrieval","task_name":"Composed Image Retrieval (CoIR)"},{"task_slug":"descriptive","task_name":"Descriptive"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"zero-shot-composed-image-retrieval-zs-cir","task_name":"Zero-Shot Composed Image Retrieval (ZS-CIR)"}],"methods":[],"datasets_introduced":[{"slug":"pattercom","name":"PatternCom","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/zero-shot-composed-image-retrieval-zs-cir-on-10","task":"Zero-Shot Composed Image Retrieval (ZS-CIR)","dataset":"PatternCom","model":"WeiCom (RemoteCLIP)","rank_in_archive_order":1,"of":2,"metrics":{"mAP":"30.19"},"uses_additional_data":false},{"leaderboard":"/sota/zero-shot-composed-image-retrieval-zs-cir-on-10","task":"Zero-Shot Composed Image Retrieval (ZS-CIR)","dataset":"PatternCom","model":"WeiCom (CLIP)","rank_in_archive_order":2,"of":2,"metrics":{"mAP":"24.83"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2405.15587","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}