代码库转移与视觉到语言翻译用于矢量量化
IEEE transactions on pattern analysis and machine intelligence
|February 25, 2026
概括
本研究介绍了VQCT-VLT,这是一个框架,将代码库从预训练的语言模型转移到矢量量化 (VQ) 以改进图像合成. 它通过利用语义关系和视觉语言对齐来解决代码书的崩,以实现强大的代码书学习.
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:
- 矢量量化 (VQ) 对于图像合成至关重要,它将图像表示为离散的令牌.
- 目前的VQ方法因从头开始学习和代码独立方法而扎于代码书的崩.
- 预先训练的语言模型拥有有价值的,但未得到充分利用的,代码书信息.
研究的目的:
- 提出一个新的代码库传输框架 (VQCT-VLT) 用于强大的矢量量化.
- 利用预训练的语言模型的代码书来克服VQ的挑战.
- 通过对齐视觉和语言语义来增强VQ,以实现卓越的图像合成.
主要方法:
- 开发了一个代码本转移框架 (VQCT-VLT),利用预训练的语言模型和部分语音知识.
- 使用语言模型的先验来构建与视觉相关的代码书,以便有效地传输代码书.
- 集成了一个视觉到语言翻译模块,配有图像字幕,用于视觉语言对齐的代码书学习.
主要成果:
- 与最先进的VQ方法相比,VQCT-VLT方法在图像合成任务中表现出卓越的性能.
- 成功地从语言模型中转移了经过良好训练的代码库,提高了VQ的稳定性.
- 在编码书学习中实现视觉语言对齐,提高语义相关性.
结论:
- 通过从预训练的语言模型转移知识,VQCT-VLT提供了一种强大的矢量量化方法.
- 该框架有效地减轻了代码书的崩,并提高了图像合成质量.
- 视觉语言对齐是开发语义上有意义和高性能VQ系统的关键.
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