对象引导的对比语言-图像预训练,用于零射击目标识别
1Southwest China Institute of Electronic Technology, Chengdu, 610036, Sichuan, China. zch.512@163.com.
Scientific reports
|January 28, 2026
概括
对象指南CLIP (OG-CLIP) 通过改进目标识别来增强安全的视觉语言模型. 它通过知识图,以区域为中心的处理和适应性功能实现了更高的准确性,优于标准模型.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的视觉语言模型 (VLM) 在语义数据,背景抑制和安全应用的多分辨率功能处理方面存在局限性.
- 作为一个著名的VLM,CLIP在识别各种目标方面表现出限制,原因是训练数据语义和硬性特征处理不足.
研究的目的:
- 引入对象指南CLIP (OG-CLIP),这是一个先进的VLM,旨在在安全系统中实现卓越的目标识别.
- 通过整合新的数据增强,以目标为中心的处理和适应性特征表示来克服现有的VLMs的局限性.
主要方法:
- 以知识图驱动的数据增强,使用5000类军事知识图和100万个图像-文本对.
- 一个以目标为中心的感兴趣区域 (ROI) 模块,将SAM 2口罩与ViT功能融合在一起,以增强焦点和背景抑制.
- 具有连续功能和动态维度权重的自适应多分辨率学习 (MRL),以实现灵活的细分化.
主要成果:
- 在99个目标类别中,OG-CLIP实现了84.28%的平均精度 (mAcc),超过了基线CLIP的11.36个百分点.
- 废弃性研究证实了每个拟议组件对整体性能的重大贡献.
- 该模型在复杂的场景中表现出卓越的性能,突出显示了它的稳定性.
结论:
- OG-CLIP为军事目标识别提供了一个可扩展和适应的视觉语言建模框架.
- 提议的改进有效地解决了语义限制,背景噪音和传统VLM的功能不灵活性.
- 未来的研究方向包括扩大数据集和优化模型以提高效率.
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