用可控制和可适应的长度级别进行图像标题
IEEE transactions on pattern analysis and machine intelligence
|November 6, 2023
概括
本研究介绍了可控制的图像标题模型,具有可调节的细节级别. 一个新的非自动回归模型提供了高效,多样化的标题,与人类质量相匹配.
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:
- 传统的图像标题模型优先考虑质量而不是风格控制.
- 控制标题细节 (长度和复杂性) 是一个重大挑战.
- 现有的自回归模型面临着更长的标题的计算复杂性问题.
研究的目的:
- 为了提高图像标题的可控性,用于不同级别的细节.
- 开发高效的非自行回归模型,用于多样化的标题生成.
- 为了弥合非自回归和自回归模型之间的性能差距.
主要方法:
- 集成的长度级嵌入用于详细或简洁的标题生成.
- 引入了一个长度级重排列变压器,用于图像-文本复杂性相关性.
- 开发了一个具有恒定计算复杂性的非自行回归 (NAR) 模型.
- 雇员精炼序列培训和序列级知识蒸.
主要成果:
- 在MS COCO数据集上实现了标题质量的新标准.
- 在生成的标题中展示了增强的可控性和多样性.
- 在可控制性和多样性方面,NAR模型的表现优于自回归 (AR) 模型.
- 对于更长的标题,NAR模型显示效率有所提高.
结论:
- 拟议的模型显著提升可控和多样化的图像标题.
- NAR模型为AR模型提供了一个更高效和多功能替代方案.
- 先进的培训技术使NAR模型能够实现具有竞争力的标题质量.
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