作者提出的方法叫做 Multi-scale Attention Network(MAN),总体框架如下图所示。核心模块为MAB,是一个 Transformer block,由 attention 和 FFN 组成。其中,attention 为 MLKA,FFN 为 GSAU。需要注意的是,最后还使用了一个LKAT,下面分别进行详细介绍。 1、Multi-scale Large Kernel Attention (MLKA) MLKA首先使用 ...
作者提出的方法叫做 Multi-scale Attention Network(MAN),总体框架如下图所示。核心模块为MAB,是一个 Transformer block,由 attention 和 FFN 组成。其中,attention 为 MLKA,FFN 为 GSAU。需要注意的是,最后还使用了一个LKAT,下面分别进行详细介绍。 1、Multi-scale Large Kernel Attention (MLKA) MLKA首先使用 ...
论文地址:Multi-scale attention network for image super-resolution - ScienceDirectWang、Xiaoguang Liu 单位:南开-百度联合实验室,中国南开大 研究动机动机源于卷积神经网络(CNN)在高级计算机视觉任务中与基于变换器的方法竞争时存在的局限性,尤其是在低级视觉任务如超分辨率上,变换器因其自注意力机制在长距离建模上的...
采用了VGG16的backbone,比较三种contribution的效果,发现采用Multi density map+Mask-attention即(+M),以及Img Res(resize到1080P),带来的效果最明显,加了scale-aware loss效果不明显。
MSA-Net Multi-Scale Attention Network for Crowd Counting 2019 作者:亚马逊 论文:https://arxiv.org/abs/1901.06026 创新点: 在backbone中就产生了多尺度的density map,经过上采样后,加入软注意力机制进行加权叠加。 提出了一个scale-aware loss,但是实验结果好像表明效果不大。... ...
In this paper, we propose a Multi-scale Hybrid Attention Network called MHANet to solve crowd counting challenges more effectively. To address the issue of scale variation, we have developed a Multi-scale Aware Module (MAM) that incorporates multiple sets of dilated convolutions with varying ...
To efficiently balance model complexity and performance, we propose a multi-scale attention network (MSAN) by cascading multiple multi-scale attention blocks (MSAB), each of which integrates a multi-scale cross block (MSCB) and a multi-path wide-activated attention block (MWAB). Specifically, ...
Object detection remains a challenging task in computer vision due to the tremendous extent of changes in the appearances of objects caused by clustered backgrounds, occlusion, truncation, and scale change. Current deep neural network (DNN)-based object detection methods cannot simultaneously achieve a...
为此,我们提出了用于无参考图像质量评估的多维关注网络(MANIQA),以提高基于gan的失真性能。 首先通过ViT提取特征,然后提出了 Transposed Attention Block (TAB) and the Scale Swin Transformer Block (SSTB)。这两个模块分别应用跨渠道和空间维度的注意机制。在这种多维度的方式下,这些模块合作增加了图像在全部和局部...
We propose a novel network named Multi-scale Attention-Net with the dual attention mechanism to enhance the ability of feature representation for liver and tumors segmentation 我们提出了一种新的具有双重注意机制的多尺度注意网络,以增强肝脏和肿瘤分割的特征表示能力。