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对视觉感知进行高效的空间交互
IEEE transactions on pattern analysis and machine intelligence
|August 28, 2025
概括
研究人员开发了递归门式卷积 (g nConv),以有效地实现使用卷积的关键视觉转换器功能. 这项新操作增强了各种视觉模型,提高了图像识别,三维分析和视觉语言任务的性能.
科学领域:
- 计算机视觉
- 深度学习
- 人工智能
背景情况:
- 视觉转换器 (ViT) 通过自我注意的空间建模取得成功.
- 卷积神经网络 (CNN) 是计算机视觉的基础.
- 将ViT的优势融入CNN是一个活跃的研究领域.
研究的目的:
- 引入一个基于卷积的框架,
- 为高阶空间交互开发一种新型操作,即递归门式卷积 (g nConv).
- 为各种视觉任务创建多功能骨干 (HorNet,Hor3D,HorCLIP).
主要方法:
- 为高效,高阶的空间交互而提出的递归卷 (g nConv).
- 开发了通用的视觉骨干:HorNet (图像识别),Hor3D (点云),HorCLIP (视觉语言).
- 将g nConv集成到现有架构中作为一个插即用模块.
主要成果:
- 在图像网,COCO和ADE20K上,HorNet的表现优于Swin Transformers和ConvNeXt.
- 通过减少计算,g nConv可以改进密集的预测任务.
- Hor3D在3D语义细分方面表现出效率; HorCLIP在视觉语言任务方面表现出色.
结论:
- 它有效地结合了ViT和CNN的优点,为视觉建模提供了一个新的基本操作.
- 拟议的HorNet家族表现出强大的性能和可扩展性.
- 通过g nConv进行高阶空间交互对各种视觉模式和任务有好处.
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