双模式 个人意识 视觉语言模型的快速调整
概括
双模式个人意识的提示调整 (BIP) 通过将先前的知识纳入提示来增强视觉语言模型. 这种新的方法提高了对各种下游任务的概括性和区分能力.
科学领域:
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 快速调整适应视觉语言模型 (VLMs) 用于诸如域泛化等任务.
- 现有的方法从训练数据中推断提示令牌,限制适应测试数据分布.
- 语境优化方法提高了概括性,但与测试集特定分布作斗争.
研究的目的:
- 引入双模式个人意识的快速调整 (BIP) 以提高VLM的适应性.
- 将个人先前的知识纳入可学习提示中,以提高可区分性和概括性.
- 开发一种新的方法,使VLM能够更有效地适应各种下游任务.
主要方法:
- BIP使用文本知识嵌入 (TKE) 和视觉知识嵌入 (VKE) 模型.
- 将类意识的文本知识项目转化为类意识提示 (TCP) 和实例意识的知识转化为实例意识提示 (VIP).
- TCP 动态调整未见域的分类器; VIP 增强视觉嵌入的可辨性.
主要成果:
- 在四个不同的任务中,BIP在15个基准指标中表现优异.
- 该方法在概括和区分能力方面取得了显著的改进.
- 评估证实了BIP作为一个plug-and-play模块的有效性.
结论:
- BIP提供了一种新且有效的方法,用于对VLMs进行快速调整.
- 整合个人先前知识显著提高模型性能.
- BIP 作为一个多功能且易于集成的模块,用于现有的 VLM 框架.
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