-VLM:视觉语言任务的全合一预训练模型
IEEE transactions on pattern analysis and machine intelligence
|December 13, 2023
概括
本研究介绍了X2-VLM,这是多粒度视觉语言预培训的统一框架. 它在图像-文本和视频-文本任务上实现了最先进的性能,在语言和域之间展示了强大的可转移性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 现有的视觉语言预训练方法主要集中在图像-文本对齐上.
- 一些方法利用对象探测器进行对象级视觉语言对齐.
- 在同时学习多粒度对齐和本地化方面存在差距.
研究的目的:
- 为多粒度视觉语言对齐提出统一的预培训框架.
- 介绍X2-VLM,一个整体模型,统一图像文本和视频文本预训.
- 为了使无限的视觉概念与各种文本描述的学习.
主要方法:
- 开发了一个统一的预训练框架,使得同时实现多粒度对齐和多粒度本地化.
- 介绍了X2-VLM,一个灵活的,模块化的,全合一的模型架构.
- 在一个单一的模型中整合了图像文本和视频文本预训.
主要成果:
- X2-VLM在基础和大规模的图像文本和视频文本任务中实现了最先进的性能.
- 该模型展示了性能和模型规模之间的有利权衡.
- 展示了高可转移性,在没有多语言预训练的情况下使用XLM-R时表现优于多语言模型.
结论:
- X2-VLM为视觉语言预培训提供了一种强大而通用的方法.
- 模块化设计使其更容易适应各种语言和领域.
- 统一框架推动了多模式学习领域的发展.
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