PanoGen++:为视觉和语言导航创建域调整的文本引导的全景环境
Sen Wang1, Dongliang Zhou2, Liang Xie3
1College of Intelligence and Computing, Tianjin University, Tianjin, 300354, China; Tianjin Artificial Intelligence Innovation Center, Tianjin, 300450, China.
概括
PanoGen++为视觉和语言导航 (VLN) 任务生成多样化的全景环境,克服数据稀缺. 该框架可以在复杂的导航场景中提高代理性能和概括性.
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:
- 视觉语言导航 (VLN) 代理需要广泛的训练数据来使用自然语言在3D环境中导航.
- 数据稀缺是一个重要的瓶,阻碍了VLN研究的进步和泛化.
研究的目的:
- 引入PanoGen++,这是一个创新的框架,用于为VLN任务生成多样化和相关的全景培训环境.
- 通过创建合成但相关的环境来解决VLN中有限的培训数据的挑战.
主要方法:
- 使用预先训练的扩散模型,具有特定域微调和参数高效的低级调整.
- 研究了用于创建新型环境的蒙面图像 inpainting 和用于空间关系学习的递归图像 outpainting.
主要成果:
- 在房间对房间 (R2R) 排行榜上取得了2.44%的成功率增长.
- 在房间对房间 (R4R) 验证未见集上,性能提高了0.63%.
- 在合作视觉和对话导航 (CVDN) 验证未见集中,提高了0.75米的目标进展.
结论:
- PanoGen++有效地增加了培训数据的多样性和对VLN的相关性.
- 该框架带来了导航代理的更好的通用化和有效性.
- 在多个基准VLN数据集中展示了显著的性能增长.
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