视觉语言 场景关系意识 零拍摄 标题词
概括
这项研究引入了一项新的场景关系级预训练任务,用于零拍摄的图像标题. 拟议的视觉语言场景关系意识标题 (SRACap) 通过专注于场景关系来增强图像理解并减少标题幻觉.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 零拍摄图像标题利用预先训练的视觉语言模型 (VLM) 和语言模型 (LM) 来生成没有配对训练数据的标题.
- 现有的方法专注于句子级或实体级的连接,但由于偏见的关联,经常患有幻觉.
- 需要改进的方法,可以在不同领域生成准确和上下文相关的标题.
研究的目的:
- 提出一个新的场景关系级预训练任务,用于零拍摄的图像标题.
- 引入视觉语言场景关系感知标题 (SRACap) 以改善图像理解和标题生成.
- 在图像标题中增强跨域零射击概括能力.
主要方法:
- 开发了场景关系级预训练任务,将场景关系视为视觉和文本模式之间的关键桥梁.
- 构建了SRACap,这是一个预测场景关系并生成标题的模型,具有场景强化切换管道以进行概括.
- 采用场景政策网络来动态裁剪突出图像区域,以及混合奖励 (MoR) 模块与专家CLIP模型,通过政策梯度算法进行优化.
主要成果:
- SRACap展示了强大的跨域零射击泛化能力.
- 该模型准确地理解场景结构,并生成高质量的字幕.
- 广泛的实验表明,SRACap在标准基准上明显优于现有的零射击推断方法.
结论:
- 场景关系级预训练方法有效地解决了零拍摄图像标题的先前方法的局限性.
- SRACap提供了一个强大的解决方案,用于生成准确,语义一致和上下文相关的图像字幕.
- 拟议的方法推进了零拍摄图像标题的最先进状态,特别是在跨域泛化方面.
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