COME:一个协作优化框架与低级MoE用于室内3D对象检测
概括
我们介绍了COME,这是一个用于室内3D物体检测的协作框架. 它独特地将通用几何属性与域特定特征结合在一起,改善跨域性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 室内3D物体检测对于计算机视觉和机器人技术至关重要.
- 当前的方法经常训练特定领域的模型,忽视了通用几何属性.
- 这种方法限制了跨不同数据集的性能.
研究的目的:
- 为室内3D物体检测提出 COME,一个协作优化框架.
- 整合普遍的几何属性,同时保持特定领域的特征.
- 为了提高跨域对象检测性能.
主要方法:
- COME使用跨领域专家参数共享策略 (CEPSS),灵感来自专家混合 (MoE).
- CEPSS拥有双重专家:对于通用属性来说是域共享的,对于独特特征来说是域特定的.
- 一个轻量级的门网动态选择专家,优化不同的领域和减少梯度冲突. 低级结构可以提高计算效率.
主要成果:
- COME在基准数据集上取得了最先进的结果.
- 与现有的多域检测方法相比,该框架显示出更高的性能.
- 它显示了可接受的参数增长,同时提高了检测准确性.
结论:
- COME有效地整合了通用和特定领域的功能,用于增强的3D对象检测.
- 拟议的框架为计算机视觉任务的跨领域学习提供了显著的进步.
- COME为室内3D物体检测提供了一个计算高效和高性能解决方案.
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