通过视觉语言预训练来促进体内推理的零射击通用化
概括
这项研究通过使用视觉语言预训练和掩面场景图形建模来增强体现的人工智能 (EAI). 这种方法改善了对在3D环境中遇到新对象的代理人的零射击概括.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
背景情况:
- 嵌入式人工智能 (EAI) 代理通过与3D环境的互动来学习任务.
- 一个关键的挑战是由于扩展对象类别而处理未见的对象,需要强大的零射击概括.
- 目前的方法依赖于昂贵的人类注释,限制了实际应用.
研究的目的:
- 为了促进EAI中体现的推理的零射击概括.
- 开发一种具有成本效益的方法,用新型对象提高代理性能.
- 利用预先训练有素的模型来整合常识知识.
主要方法:
- 利用视觉语言的预训练来编码一般的先前知识和常识.
- 实现面具场景图形建模 (MSGM),用于自主监督预先训练的表示的纠正.
- 采用代信息传递来学习特定任务的知识.
主要成果:
- 在各种体现的推理任务中取得了显著的改进.
- 在MP3D-EQA数据集上的答案准确度增加了5.0%以上.
- 建立了新的最先进的表演,特别是在现实世界的场景上,有许多新奇的对象.
结论:
- 拟议的方法有效地增强了对嵌入式代理的零射击概括.
- 视觉语言预培训与MSGM相结合,为EAI提供了一种强大的方法.
- 这项工作为现实世界EAI应用提供了可扩展和高效的解决方案.
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