在光谱领域进行参数高效的微调,用于点云学习
概括
PointGST是一个新的参数效率微调方法,用于点云模型. 它显著降低了培训成本,并通过调整光谱领域的模型来优于完全微调.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 3D数据处理 3D数据处理
背景情况:
- 预训练增强了点云模型,但需要计算上昂贵的完整微调.
- 现有的方法是存储密集型的,需要大量的计算资源来完成下游任务.
研究的目的:
- 为点云模型引入一种新的参数高效微调 (PEFT) 方法.
- 解决与完整微调相关的存储和计算挑战.
- 提高将一般知识转移到下游点云任务的效率.
主要方法:
- 提议PointGST (点云图谱调),一种结预训练模型的PEFT方法.
- 引入一个轻量级的点云光谱适配器 (PCSA) 用于光谱域微调.
- 将转移点令牌传送到光谱域,以消除空间混乱的相关性,并纳入特定任务的内在信息.
主要成果:
- 在具有挑战性的点云数据集上,PointGST的性能优于完全微调.
- 实现了卓越的准确性:99.48% (ScanObjNN OBJ_BG),97.76% (OBJ_ONLY),96.18% (PB_T50_RS). 这是一个非常好的方法.
- 将可训练的参数减少到总数的0.67%,建立了一个新的最先进的状态.
结论:
- 通过将培训成本降至最低,PointGST为点云学习提供了高效的解决方案.
- 频谱域适应有效地将一般知识转移到下游任务中.
- 在显著降低参数要求的情况下实现了最先进的性能.
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