Multi-scale Residual Block (MSRB) was designed. MSRB combined a multi-scale convolution module and residual connection to improve the feature extraction capability of the network. Multi-scale Attention Module (MSAM) was proposed, which could effectively strengthen useful features and suppress useless fe...
为了解决上述问题,我们设计了一种多尺度扩张残差块(MDRB)fMDRB multi-scale dilated residual block (MDRB),它不仅可以有效地扩大感受野 receptive field 以感知帧之间的大像素运动, 还可以 在扩张卷积的帮助下可以很好地保留对象边界细节 捕获多尺度上下文信息。 具体的是: 首先堆叠两个 3 × 3 和 5 × 5 卷...
Residual blocks are a common choice for model architectures in the current work. Figure4shows a comparison between three commonly used residual blocks: the Basic block, the Bottleneck block, and the Res2Net block53. These blocks are incorporated into various model architectures currently in use. H...
Multi-scale Transformer 希望定位篡改伪影与其他区域不一致,因此需要建模长期关系,计算相似度。 引入多尺度的transformer,覆盖不同大小的区域 输入图片(HW3)再backbone提取shallow feature,然后分成不同的尺寸(不同的头)计算patch-wise的self attention,即每个patch(rh*rh*c)展成一维向量,使用fc层embed到query embedding...
Pulmonary nodules are the main manifestation of early lung cancer. Therefore, accurate detection of nodules in CT images is vital for lung cancer diagnosis. A 3D automatic detection system of pulmonary nodules based on multi-scale attention networks is p
文章: Residual Attention: A Simple but Effective Method for Multi-Label Recognition, ICCV2021 下面说一下我对这篇文章的浅陋之见, 如有错误, 请多包涵指正. 文章的核心方法 如下图所示为其处理流程: 图中X 为CNN骨干网络提取得到的feature, 其大小为 d*h*w , 为1个batch数据. 一般 d*h*w=2048*7...
A Novel Multi-scale Key-Point Detector Using Residual Dense Block and Coordinate Attention 来自 Semantic Scholar 喜欢 0 阅读量: 62 作者:LD Kuang,J Tao,J Zhang,F Li,X Chen 摘要: Object detection, one of the core missions in computer vision, plays a significant role in various real-life ...
A multi-scale residual attention model is proposed to achieve single image super-resolution reconstruction. The model consists of shallow feature extraction, multi-scale residual attention network and reconstruction. The convolution kernels of different scales are used for feature extraction of low-...
residual connections to extract high-dimensional feature information. The Position-wise Attention Block is used to capture the spatial dependencies of feature maps, and the Multi-scale Fusion Attention Block is to aggregate the channel dependencies between any feature maps via fusing High and Low-...
In the feature extraction, we utilize a hierarchical feature fusion block to extract the multi-scale features. Furthermore, we adopt an attention mechanism to obtain the local discriminative parts of feature maps. In the classification layer, we utilize a fully convolutional classifier to generate ...