MTA 是 Multi-scale Token Aggregation 的缩写,i 是 head 索引,r 为降采样的步伐。之后还会专门对 V 做一个局部增强,其实就是一个 depth-wise 卷积。在实验中 r 都只设成了两种,前一半的 head 负责一个尺度,后一半 head 负责一个尺度。可以参考一下代码: if sr_ratio==8: #以 r 取 4/8 为例 self...
最近的视觉变换器(ViT)模型在各种计算机视觉任务中取得了令人鼓舞的结果,这要归功于它通过自我注意对图像斑块或标记物的长距离垂线进行建模的能力。然而,这些模型通常指定每层内每个标记特征的相似受体字段。…
为了解决这个问题,作者提出了一个新颖且通用的策略—shunted self-attention(SSA)。SSA 的关键思想是将异构感受野大小注入标记:在计算自注意力矩阵之前,它选择性地合并标记以表示更大的对象特征,同时保持某些标记以保留细粒度特征。这种新颖的合并方案使 self-attention 能够学习不同大小的对象之间的关系,同时降低令牌数...
Guided-attention and gated-aggregation network for medical image segmentation 2024, Pattern Recognition Citation Excerpt : Multi-scale feature fusion is an effective way to improve the quality of the features to handle scale variations, and can be achieved via feature combination of high-level and lo...
论文阅读:Hierarchical multi-scale attention for semantic segmentation,程序员大本营,技术文章内容聚合第一站。
Conventional local attention operates on a single window scale and captures interactions within the window. However, the key points that need to be attended to have significant variations in their distribution due to the diverse size of the head and the shape and size of the adenoids in the lat...
论文阅读笔记二十一:MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS(ICRL2016) 论文源址:https://arxiv.org/abs/1511.07122 tensorflow Github:https://github.com/ndrplz/dilation-tensorflow 摘要 该文提出了空洞卷积模型,在不降低分辨率的基础上聚合图像中不同尺寸的上下文信息,同时,空洞卷积扩大感受野的...
Transformer-based trackers greatly improve tracking success rate and precision rate. Attention mechanism in Transformer can fully explore the context infor
Zheng, “TransVPR: Transformer-based place recognition with multi-level attention aggregation.” [3] A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv Prepr. arXiv2010.11929, 2020. 本文使用 Zhihu On VSCode 创作并发布...
Multi-ScaleVision Longformer 论文链接:https://arxiv.org/pdf/2103.15358.pdf 提出了一个可以处理高分辨率图像的transformer结构 主要有两点: (1) 多尺度结构 (2) Vision Longformer的attention机制来获得关于token数目线性的计算量。 Efficient ViT (E-ViT) ...