价值:视觉-音频-语言全感知预训模型和数据集
IEEE transactions on pattern analysis and machine intelligence
|October 17, 2024
概括
我们介绍了VALOR,这是一个新的视觉-音频-语言全视觉模型,用于多式联络任务. 通过共同学习视觉,音频和语言关系,VALOR 实现了最先进的结果.
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:
- 现有的预培训模型主要集中在视觉语言任务上.
- 有需要的模型,可以共同理解和生成跨视觉,音频和语言模式的内容.
研究的目的:
- 提出和评估视觉-音频-语言全感知 (VALOR) 预训练模型.
- 通过共同建模视觉,音频和语言来实现端到端的多式联络理解和生成.
主要方法:
- VALOR使用独立的视觉,音频和语言编码器,以及解码器来生成文本.
- 两个借口任务,多模式分组对齐 (MGA) 和多模式分组标题 (MGC),是为预训设计的.
- 创建了一个大规模的数据集,VALOR-1M,包括100万个带有注释标题的可听视频.
主要成果:
- 价值有效地学习视觉,音频和语言之间的强烈相关性.
- 该模型展示了各种下游任务的概括能力,如检索,标题和问题答案.
- 在多个跨模式的基准指标上,VALOR取得了新的最先进的表现.
结论:
- VALOR代表了多式联络预培训研究的重大进展.
- 视觉,音频和语言的联合建模为AI理解和生成开辟了新的途径.
- 开发的VALOR-1M数据集有助于进一步研究三模式学习.
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