param_routing=self.param_routing)ifself.soft_routing:# soft routing, always diffrentiable (if no detach)mul_weight ='soft'elifself.diff_routing:# hard differentiable routingmul_weight ='hard'else:# hard non-differentiable routingmul_weight ='none'self.kv_gather = KVGather(mul_weight=mul_weig...
相反,我们提出了一种简单的解决方案,通过收集键/值标记来处理,其中只涉及到对于硬件友好的稠密矩阵乘法。我们将这种方法称为双层路由注意力(Bi-level Routing Attention,简称BRA),因为它包含了一个区域级别的路由步骤和一个标记级别的注意力步骤。 总结->引入了一种新颖的双层路由机制来改进传统的注意力机制,以适应查...
因此,本文通过双层路由(bi-level routing)提出了一种新颖的动态稀疏注意力(dynamic sparse attention),以实现更灵活的计算分配和内容感知,使其具备动态的查询感知稀疏性,如图(f)所示。 基于BRA模块,本文构建了一种新颖的通用视觉转换器BiFormer。如上图所示,其遵循大多数的vision transformer架构设计,也是采用四级金字塔...
Bi-Level Routing Attention (BRA)是一种注意力机制,旨在解决多头自注意力机制(MHSA)的可扩展性问题。传统的注意力机制要求每个查询都要关注所有的键-值对,这在处理大规模数据时可能会导致计算和存储资源的浪费。BRA通过引入动态的、查询感知的稀疏注意力机制来解决这一问题。 BRA的关键思想是在粗粒度的区域级别上过...
简介:YOLOv5改进有效涨点系列->适合多种检测场景的BiFormer注意力机制(Bi-level Routing Attention) 一、本文介绍 BiFormer是一种结合了Bi-level Routing Attention的视觉Transformer模型,BiFormer模型的核心思想是引入了双层路由注意力机制。在BiFormer中,每个图像块都与一个位置路由器相关联。这些位置路由器根据特定的...
本文提出新颖的注意力DSAM,创新度极佳,效果优于CBAM。通过将Channel Attention+Spatial Attention升级为Deformable Bi-level Attention+Spatial Attention,解决了BRA注意力问题点。实验表明,YOLO11-seg模型在道路裂纹分割数据集上...
BRA-YOLO: Object Detection Algorithm with Bi-Level Routing Attention for YOLOv5doi:10.1007/978-981-97-4393-3_25At present, YOLOv5 is the most popular algorithm in single-stage target detection, has covered all areas of society. However, because the neck layer can not effectively integrate the...
We refer to this approach as Bi-level Routing Attention (BRA), as it contains a region-level routing step and a token-level atten- tion step. By using BRA as the core building block, we propose BiFormer, a general vision transformer backbone that can be used for ...
BiFormer: Vision Transformer with Bi-Level Routing Attention. CVPR 2023. Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, and Rynson Lau News 2023-04-11: object detection code is released. It achives significantly better results than the paper reported due to a bug fix. 2023-03-24: For...
This article proposes a SC method that utilizes bi-level routing attention (BRA), named BRASC. By capturing both region-to-region and token-to-token attention mechanisms, BRA captures broad meaning as well as specific details, retaining key semantic information while eliminating less relevant data...