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<p>Target recognition is critical for security systems, but traditional Visual-Language Models (VLMs) like CLIP suffer from limited training data semantics, poor background suppression, and inflexible multi-resolution features. To address these, we propose Object-Guide CLIP (OG-CLIP), integrating three core enhancements: <b>Knowledge graph-driven data augmentation</b>: A 5000-category military knowledge graph and 1M image-text pairs via multi-source acquisition and knowledge-infused prompts. <b>Target-centered ROI module</b>: Fuses SAM 2-generated masks with ViT features to focus on discriminative regions and suppress background noise. <b>Adaptive MRL</b>: Resolves traditional MRL’s rigid granularity via 128D–1024D 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.</p>

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Object-guided contrastive language-image pre-training for zero-shot target recognition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月28日

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  • Chenghao Zheng

摘要

Target recognition is critical for security systems, but traditional Visual-Language Models (VLMs) like CLIP suffer from limited training data semantics, poor background suppression, and inflexible multi-resolution features. To address these, we propose Object-Guide CLIP (OG-CLIP), integrating three core enhancements: Knowledge graph-driven data augmentation: A 5000-category military knowledge graph and 1M image-text pairs via multi-source acquisition and knowledge-infused prompts. Target-centered ROI module: Fuses SAM 2-generated masks with ViT features to focus on discriminative regions and suppress background noise. Adaptive MRL: Resolves traditional MRL’s rigid granularity via 128D–1024D 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.