{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:55:52Z","timestamp":1760230552465,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SAD 2021-ROMMEO","award":["21007759"],"award-info":[{"award-number":["21007759"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vehicle detection is an important but challenging problem in Earth observation due to the intricately small sizes and varied appearances of the objects of interest. In this paper, we use these issues to our advantage by considering them results of latent image augmentation. In particular, we propose using supervised contrastive loss in combination with a mutual guidance matching process to helps learn stronger object representations and tackles the misalignment of localization and classification in object detection. Extensive experiments are performed to understand the combination of the two strategies and show the benefits for vehicle detection on aerial and satellite images, achieving performance on par with state-of-the-art methods designed for small and very small object detection. As the proposed method is domain-agnostic, it might also be used for visual representation learning in generic computer vision problems.<\/jats:p>","DOI":"10.3390\/rs14153689","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T23:49:27Z","timestamp":1659397767000},"page":"3689","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mutual Guidance Meets Supervised Contrastive Learning: Vehicle Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7896-5967","authenticated-orcid":false,"given":"Ho\u00e0ng-\u00c2n","family":"L\u00ea","sequence":"first","affiliation":[{"name":"Institut de Recherche en Informatique et Syst\u00e8mes Al\u00e9atoires (IRISA), Universit\u00e9 Bretagne Sud, UMR 6074, F-56000 Vannes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institut de Recherche en Informatique et Syst\u00e8mes Al\u00e9atoires (IRISA), Universit\u00e9 Rennes 1, F-35000 Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-767X","authenticated-orcid":false,"given":"Minh-Tan","family":"Pham","sequence":"additional","affiliation":[{"name":"Institut de Recherche en Informatique et Syst\u00e8mes Al\u00e9atoires (IRISA), Universit\u00e9 Bretagne Sud, UMR 6074, F-56000 Vannes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2384-8202","authenticated-orcid":false,"given":"S\u00e9bastien","family":"Lef\u00e8vre","sequence":"additional","affiliation":[{"name":"Institut de Recherche en Informatique et Syst\u00e8mes Al\u00e9atoires (IRISA), Universit\u00e9 Bretagne Sud, UMR 6074, F-56000 Vannes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H., and Fu, Y. 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