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To address these, we propose Object-Guide CLIP (OG-CLIP), integrating three core enhancements:\n                    <jats:bold>Knowledge graph-driven data augmentation<\/jats:bold>\n                    : A 5000-category military knowledge graph and 1M image-text pairs via multi-source acquisition and knowledge-infused prompts.\n                    <jats:bold>Target-centered ROI module<\/jats:bold>\n                    : Fuses SAM 2-generated masks with ViT features to focus on discriminative regions and suppress background noise.\n                    <jats:bold>Adaptive MRL<\/jats:bold>\n                    : Resolves traditional MRL\u2019s rigid granularity via 128D\u20131024D continuous features, dynamic dimension weighting, and cross-granularity semantic alignment. Experiments on 99 target categories (military aircraft, warships, civilian targets) show OG-CLIP achieves 84.28% mean Accuracy (mAcc), 11.36 percentage points higher than baseline CLIP. Ablation confirms contributions of each component, and OG-CLIP excels in complex scenarios. The proposed framework offers a scalable and adaptable vision-language modeling approach for military recognition, with future work focusing on dataset expansion and model lightweight optimization.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-36314-7","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:34:48Z","timestamp":1769574888000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Object-guided contrastive language-image pre-training for zero-shot target recognition"],"prefix":"10.1038","volume":"16","author":[{"given":"Chenghao","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"issue":"7553","key":"36314_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y. & Hinton, G. 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This repository includes the training pipeline and data preprocessing scripts. Due to confidentiality restrictions, proprietary modules cannot be released publicly. However, the provided code is sufficient to reproduce the principal results reported in this manuscript. For access to the complete codebase, including the proprietary modules,  interested users are encouraged to contact the corresponding author.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}],"article-number":"6425"}}