{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:40:21Z","timestamp":1777653621337,"version":"3.51.4"},"reference-count":59,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"College Student Innovation and Entrepreneurship Training Program","award":["202510434005"],"award-info":[{"award-number":["202510434005"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202281"],"award-info":[{"award-number":["62202281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2025MS1076"],"award-info":[{"award-number":["ZR2025MS1076"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Object counting in remote sensing images is valuable for applications such as urban planning and environmental monitoring. However, it remains challenging due to heterogeneous annotations, semantic ambiguity in open-vocabulary queries, and performance degradation of small targets. To address these limitations, we propose DR-CLIP (Deformable Remote CLIP), a vision\u2013language model for remote sensing image counting that incorporates deformable visual feature extraction with text-guided prediction. DR-CLIP includes a (1) Region-to-Instruction (R2I) mechanism to convert points, bounding boxes, and polygons into a unified image\u2013text training representation, a (2) Multi-scale Deformable Attention (MSDA) to enhance discriminative feature extraction across extreme scale variations and cluttered backgrounds, and a (3) Text-Guided Counting Head that establishes robust cross-modal alignment through contrastive learning, achieving open-vocabulary counting capability without category-specific retraining. On DOTA-v2.0, DR-CLIP achieves a Mean Absolute Error (MAE) of 2.34 and a Root Mean Squared Error (RMSE) of 3.89, outperforming baselines by 19.0% in MAE. The MSDA module significantly increases Small-Object Recall (SOR) to 0.824, which is especially effective in situations involving dense and small object counting. In cross-modal retrieval, DR-CLIP attains R@1 scores of 68.3% (image-to-text) and 72.1% (text-to-image) on the Remote Sensing Image Captioning Dataset (RSICD). The framework generalizes robustly, with only 8.7% performance degradation in cross-domain tests, which is significantly lower than the 23.4% drop observed in baseline methods.<\/jats:p>","DOI":"10.3390\/s26061863","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T08:41:26Z","timestamp":1773650486000},"page":"1863","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DR-CLIP: A Deformable Vision\u2013Language Model for Scale-Invariant Object Counting in Remote Sensing Images"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0014-1668","authenticated-orcid":false,"given":"Jingzhe","family":"Nie","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianze","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8459-3186","authenticated-orcid":false,"given":"Xu","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, X., and Chen, D. 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