The multi-scale receptive field and large kernel attention (LKA) module have
been shown to significantly improve performance in the lightweight image
super-resolution task. However, existing lightweight super-resolution (SR)
methods seldom pay attention to designing efficient building block with
multi-scale receptive field for local modeling, and their LKA modules face a
quadratic increase in computational and memory footprints as the convolutional
kernel size increases. To address the first issue, we propose the multi-scale
blueprint separable convolutions (MBSConv) as highly efficient building block
with multi-scale receptive field, it can focus on the learning for the
multi-scale information which is a vital component of discriminative
representation. As for the second issue, we revisit the key properties of LKA
in which we find that the adjacent direct interaction of local information and
long-distance dependencies is crucial to provide remarkable performance. Thus,
taking this into account and in order to mitigate the complexity of LKA, we
propose a large coordinate kernel attention (LCKA) module which decomposes the
2D convolutional kernels of the depth-wise convolutional layers in LKA into
horizontal and vertical 1-D kernels. LCKA enables the adjacent direct
interaction of local information and long-distance dependencies not only in the
horizontal direction but also in the vertical. Besides, LCKA allows for the
direct use of extremely large kernels in the depth-wise convolutional layers to
capture more contextual information, which helps to significantly improve the
reconstruction performance, and it incurs lower computational complexity and
memory footprints. Integrating MBSConv and LCKA, we propose a large coordinate
kernel attention network (LCAN).

提出了多尺度感受野、大内核注意力模块 (LCAN)，用于改进轻量级图像超分辨率任务，并提高重建性能。

大坐标核注意力网络用于轻量级图像超分辨率

Large coordinate kernel attention network for lightweight image  super-resolution

Few-shot semantic segmentation (FSS) offers immense potential in the field of
medical image analysis, enabling accurate object segmentation with limited
training data. However, existing FSS techniques heavily rely on annotated
semantic classes, rendering them unsuitable for medical images due to the
scarcity of annotations. To address this challenge, multiple contributions are
proposed: First, inspired by spectral decomposition methods, the problem of
image decomposition is reframed as a graph partitioning task. The eigenvectors
of the Laplacian matrix, derived from the feature affinity matrix of
self-supervised networks, are analyzed to estimate the distribution of the
objects of interest from the support images. Secondly, we propose a novel
self-supervised FSS framework that does not rely on any annotation. Instead, it
adaptively estimates the query mask by leveraging the eigenvectors obtained
from the support images. This approach eliminates the need for manual
annotation, making it particularly suitable for medical images with limited
annotated data. Thirdly, to further enhance the decoding of the query image
based on the information provided by the support image, we introduce a
multi-scale large kernel attention module. By selectively emphasizing relevant
features and details, this module improves the segmentation process and
contributes to better object delineation. Evaluations on both natural and
medical image datasets demonstrate the efficiency and effectiveness of our
method. Moreover, the proposed approach is characterized by its generality and
model-agnostic nature, allowing for seamless integration with various deep
architectures. The code is publicly available at
\href{this https URL}{\textcolor{magenta}{GitHub}}.

借鉴谱分解方法，将图像分解问题视为图划分任务，并通过特征亲和力矩阵的拉普拉斯矩阵的特征向量分析来估计感兴趣对象的分布；提出一种新颖的自监督式 FSS 框架，不依赖于任何标注，并通过利用从支持图像获得的特征向量自适应地估计查询掩码，从而消除了手动注释的需求，特别适用于具有有限标注数据的医学图像；引入了多尺度大卷积核注意力模块，进一步提高了对支持图像提供的信息进行查询图像解码的能力，通过选择性地强调相关特征和细节，改善了分割过程并有助于更好的物体勾边；在自然和医学图像数据集上的评估结果展示了我们方法的高效性和有效性。