Our model is built on a U-shaped architecture and incorporates two key innovations: a modified InceptionNeXt block and a novel Spatial-aware Channel Attention (SCA) module. The customized InceptionNeXt block enhances feature extraction by leveraging depthwise and pointwise separable convolutions, ...
(2)使用Spatial-Channel Attention module 提取multi-scale和global context features 来encode local 和global information。SCA具有空间和通道注意性,能够保证空间和通道特征的recalibrating。因此可以有效的区分特征并抑制不明显的特征。 (3)decoder:Extension Spatial Upsample module:结合低分辨率特征图和多尺度低层次特征协...
The GSCAT-UNET is an advanced UNET architecture comprising of Spatial-Channel Attention Gates(SCAG), Three Level Attention Module(TLM) and Global Feature Module(GFM) for global level oil spill feature enhancement leading to effective oil spill detection and discrimination from lookalikes. Sentinel-1 ...
we introduced a spatial-channel attention mechanism called the bottleneck attention module (BAM)16(Fig.2). This mechanism consists of two independent attention modules, the channel attention module (CAM) and the spatial attention module (SAM). To emphasize or suppress the information...
Specifically, we propose the Spatial Channel Attention Module (SCAM), which can output an enhanced fusion feature map by combining spatial and channel information from template and search region features. Finally, we feed the output of SCAM into the ranking head network for classification and ...
The attention module consists of two submodules to identify useful features and exclude harmful components in inputs from both channel and spatial dimensions, respectively. The attention-guided feature maps are sent to the encoders for further feature extraction, and then, the outputs are sent to ...
CS-Net structure diagram. It comprises of three phases: the feature encoder module, the channel and spatial attention module and the feature decoder module. (Color figure online) Full size image The proposed CS-Net consists of three phases: the encoder module, the channel and spatial attention ...
This repo is the official of implementation of "SCSA: Exploring the Synergistic Effects Between Spatial and Channel Attention". Introduction In this paper, starting from the synergy of multi-semantic information, we propose a plug-and-play Spatial and Channel Synergistic Attention module(SCSA). ...
An increasing number of researchers have been interested in self-attention-based lane detection. In difficult situations such as shadows, bright lights, and nights extracting global information is effective. Regardless of channel or spatial attention, it cannot independently extract all global information...
The main contribution of this paper is the multi-kernel-size, spatial-channel attention method (MKSC) to analyze chest X-ray images for COVID-19 detection. Our proposed method integrates a feature extraction module, a multi-kernel-size attention module, and a classification module. We use X-...