The advent of deep-learning-based registration networks has addressed the
time-consuming challenge in traditional iterative methods.However, the
potential of current registration networks for comprehensively capturing
spatial relationships has not been fully explored, leading to inadequate
performance in large-deformation image registration.The pure convolutional
neural networks (CNNs) neglect feature enhancement, while current
Transformer-based networks are susceptible to information redundancy.To
alleviate these issues, we propose a pyramid attention network (PAN) for
deformable medical image registration.Specifically, the proposed PAN
incorporates a dual-stream pyramid encoder with channel-wise attention to boost
the feature representation.Moreover, a multi-head local attention Transformer
is introduced as decoder to analyze motion patterns and generate deformation
fields.Extensive experiments on two public brain magnetic resonance imaging
(MRI) datasets and one abdominal MRI dataset demonstrate that our method
achieves favorable registration performance, while outperforming several
CNN-based and Transformer-based registration networks.Our code is publicly
available at this https URL

我们提出了金字塔注意力网络（PAN）用于可变形医学图像配准，通过引入双流金字塔编码器和局部多头注意力变换器实现特征表示和分析运动模式，取得了较好的配准性能。

金字塔注意力网络用于医学图像配准

Pyramid Attention Network for Medical Image Registration

In this paper, we present a new Mask R-CNN based text detection approach
which can robustly detect multi-oriented and curved text from natural scene
images in a unified manner. To enhance the feature representation ability of
Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention
Network (PAN) as a new backbone network of Mask R-CNN. Experiments demonstrate
that PAN can suppress false alarms caused by text-like backgrounds more
effectively. Our proposed approach has achieved superior performance on both
multi-oriented (ICDAR-2015, ICDAR-2017 MLT) and curved (SCUT-CTW1500) text
detection benchmark tasks by only using single-scale and single-model testing.

本文提出了基于 Mask R-CNN 技术的文本检测新方法，应用金字塔注意力网络作为新的 Mask R-CNN 骨干网络以提高其特征表达能力，该方法可以统一地在自然场景图像中稳健地检测多定向和曲线文本，并在多种测试数据集上获得优越的性能。

使用金字塔注意网络的 Mask R-CNN 进行场景文字检测

Mask R-CNN with Pyramid Attention Network for Scene Text Detection

A Pyramid Attention Network(PAN) is proposed to exploit the impact of global
contextual information in semantic segmentation. Different from most existing
works, we combine attention mechanism and spatial pyramid to extract precise
dense features for pixel labeling instead of complicated dilated convolution
and artificially designed decoder networks. Specifically, we introduce a
Feature Pyramid Attention module to perform spatial pyramid attention structure
on high-level output and combining global pooling to learn a better feature
representation, and a Global Attention Upsample module on each decoder layer to
provide global context as a guidance of low-level features to select category
localization details. The proposed approach achieves state-of-the-art
performance on PASCAL VOC 2012 and Cityscapes benchmarks with a new record of
mIoU accuracy 84.0% on PASCAL VOC 2012, while training without COCO dataset.

使用金字塔注意力网络 (PAN) 进行语义分割，通过特征金字塔注意力模块和全局注意力上采样模块提取精确的密集特征，优于现有方法，可达到 PASCAL VOC 2012 和 Cityscapes 数据集的最高精度 mIoU 84.0%。