补充文本引导的注意力为零射击对手的稳定性
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概括
研究人员开发了文本引导注意力为零射击稳定性 (TGA-ZSR) 和辅助文本引导注意力 (Comp-TGA),以提高视觉语言模型对抗对手攻击的稳定性. 这些方法显著提高了对清洁和对抗性示例的零射击准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 像CLIP这样的预训练有素的视觉语言模型在零射击任务中表现出色,但易受对抗性示例的影响.
- 在这些模型中,对抗性扰动可以改变文本引导注意力机制.
- 现有的方法在提高强度的同时努力保持概括性.
研究的目的:
- 提高视觉语言模型的对抗性强度,而不牺牲概括性.
- 解决在对抗场景中注意力集中在不相关特征的问题.
- 在具有挑战性的数据集中提高零射击强大的准确性.
主要方法:
- 拟议的文字引导注意力为零射击强度 (TGA-ZSR) 与本地注意力改进和全球注意力约束模块.
- TGA-ZSR将注意力地图从对抗和清洁的例子对齐,并将注意力限制在清洁数据上.
- 引入了辅助文本引导注意力 (Comp-TGA),集成了类和非类提示引导注意力,以实现全面的前景表示.
主要成果:
- TGA-ZSR在零射击强大的精度方面取得了显著的改进.
- 通过使用互补的注意力机制,Comp-TGA进一步提高了稳健性.
- 16个数据集的实验表明,TGA-ZSR和Comp-TGA相对于最先进的方法实现了9.58%和11.95%的改进.
结论:
- 在视觉语言模型中,TGA-ZSR和Comp-TGA是提高对抗性强度的有效策略.
- 互补的注意力机制为强大的零射击学习提供了一个有希望的方向.
- 提出的方法保持了概括性,同时显著提高了对抗性示例的性能.
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