以往Vision Transformer 结构在将图片转换成序列时会切成提前预设好的大小,将统一大小的小块输入网络中,但是这种方法往往忽略了图片中包含的尺度特征。本文提出了一种多尺度的转换结构,并提出间隔选取形式的 Attention 模块节约显存。 首先作者在对一张图片进行嵌入 Embedding 操作时,会选取四个不同大小的卷积核以及输出...
2.2 Dynamic Position Bias 随着位置编码技术的不断发展,相对位置编码偏差逐渐的应用到了transformers中,很多的vision transformers均采用RPB来替换原始的APE,好处是可以直接插入到我们的attention中,不需要很繁琐的公式计算,并且可学习性高,鲁棒性强,公式如下: 以Swin-Transformer为例,位置偏差矩阵B是一个固定大小的矩阵,...
[ICCV2021]CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification 狗彦祖 永远快乐9 人赞同了该文章 论文:arxiv.org/abs/2103.1489 代码:github.com/IBM/CrossViT 背景 与卷积神经网络相比,最近开发的vision transformer[1](ViT)在图像分类方面取得了很好的结果。 现有的对ViT的研究工作...
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention https://arxiv.org/abs/2108.00154 https://github.com/cheerss/CrossFormer 这是视觉的Transformer 演进过程:VIT---PVT---CrossFormer VIT没有考虑多尺度信息 PVT通过特征下采样集成了多尺度信息 CrossFormer基于跨尺度注意力机制的视觉Tra...
CrossFormer:A Versatile vision transformer Based on cross-scale attention (一种基于跨尺度注意力的多功能视觉transformer) (浙江大学、哥伦比亚大学、腾讯数据平台) Abstract Transformers在处理视觉任务方面取得了很大进展。然而,现有的vision transformer仍然不具备一种对视觉输入很... ...
the computational burden but also keeps both small-scale and large-scale features in the embeddings. Through the above two designs, we achieve cross-scale attention. Besides, we put forward a dynamic position bias for vision transformers to make the popular relative position bias apply to variable...
In this paper, we propose a cross-scale attention\n(CSA) model, which explicitly integrates features from different scales to form\nthe final representation. Moreover, we propose the adoption of the attention\nmechanism to specify the weights of local and global features based on the\nspatial ...
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification Chun-Fu (Richard) Chen, Quanfu Fan, Rameswar Panda MIT-IBM Watson AI Lab chenrich@us.ibm.com, qfan@us.ibm.com, rpanda@ibm.com Abstract The recently developed vision tra...
However, existing vision transformers still do not possess an ability that is important to visual input: building the attention among features of different scales. The reasons for this problem are two-fold: (1) Input embeddings of each layer are equal-scale without cross-scale features; (2) ...
super(CrossScaleAttention, self).__init__() self.ksize = ksize self.stride = stride self.softmax_scale = softmax_scale self.scale = scale self.average = average escape_NaN = torch.FloatTensor([1e-4])self.register_buffer('escape_NaN', escape_NaN) ...