交叉形式++:一个多功能视觉转换器,依赖于交叉规模的注意力
IEEE transactions on pattern analysis and machine intelligence
|December 19, 2023
概括
本研究介绍了CrossFormer,这是一款新的视觉变换器,明确结合了多层次特征. 通过解决变压器关键局限性,CrossFormer++在各种计算机视觉任务中展示了卓越的性能.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 人工智能的人工智能
背景情况:
- 现有的视觉转换器没有明确利用视觉感知至关重要的多尺度特征.
- 在当前的变压器架构中,处理不同规模的特征仍然是一个挑战.
研究的目的:
- 提出一种新的视觉转换器架构,CrossFormer,可以有效地集成多个尺度的功能.
- 解决与自我注意机制和特征表示有关的视觉变压器的性能限制.
主要方法:
- 引入了一个跨尺度嵌入层 (CEL),以将令牌与多尺度补丁混合在一起.
- 开发了一个长短距离注意力 (LSDA) 机制来管理计算负载并保留规模特定的特征.
- 提出了一个渐进式组大小 (PGS) 范式和一个振幅冷却层 (ACL),以减轻扩大的自我注意力图和振幅爆炸.
主要成果:
- 克罗斯福默有效地结合了跨度特征,增强了自我注意能力.
- LSDA减少了计算复杂性,同时保留了小规模和大规模的特征.
- PGS和ACL成功地解决了分别扩大自我注意地图和振幅爆炸的问题.
结论:
- 在图像分类,对象检测,实例细分和语义细分方面,CrossFormer++显著优于现有的视觉转换器.
- 拟议的架构和技术为各种视觉任务的视觉变压器设计提供了更强大,更有效的方法.
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