户外视觉和语言导航需要对象水平对齐
Yanjun Sun1,2, Yue Qiu2, Yoshimitsu Aoki1
1Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一个对体AI代理的对象级对齐模块 (OAlM). OAlM通过使用地标作为子目标来改善户外视觉和语言导航 (VLN),提高复杂数据集的性能.
科学领域:
- 嵌入式的人工智能 (AI)
- 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:
- 视觉和语言导航 (VLN) 是体现人工智能的复杂多模式任务,在户外城市环境中尤其具有挑战性.
- 现有的户外VLN模型经常与复杂的环境和详细的指令解释作斗争,导致导航失败.
- 人类导航有效地利用地标作为参考点,以更有效地寻找路径.
研究的目的:
- 为了提高室外视觉和语言导航 (VLN) 在嵌入式AI中的性能.
- 解决当前模型在解释复杂环境和指令方面的局限性.
- 开发一种更强大,更适应性的导航代理,其灵感来源于基于人类地标的导航.
主要方法:
- 提出了一个对象级对齐模块 (OAlM),以指导代理人识别和使用指令指定的对象作为地标.
- 将公认的地标视为中间子目标,以将远程导航路径分解为更短,更容易管理的段落.
- 整合了OAlM与现有的全景和指令基于特征的VLN模型.
主要成果:
- 与基线模型相比,OAlM展示了更以对象为中心的导航策略.
- 拟议的方法显著超过了具有挑战性的户外VLN触摸数据集的基线.
- 在任务完成率 (TC) 中实现了3.19%的改善,这表明导航效率和成功得到了提高.
结论:
- 作为子目标,利用对象级信息和地标是一个有前途的策略,用于改进嵌入式AI导航.
- 在复杂,不可预测的现实环境中,OAlM提高了代理商的适应性和稳定性.
- 这项研究为更先进,更高效的室外导航系统铺平了道路.
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