(CNN). The proposed method contains three parts: short-time gap feature extraction, multi-scale local feature learning, and global feature learning. In the process of short-time gap feature extraction, large kernel filters are employed to extract the features within the short-time gap from the ...
The second challenge is addressed by a multiview CNN fusion model through a combination layer connecting the representation layers of RGB view and depth view. Comprehensive experiments on four benchmark datasets demonstrate the significant and consistent improvements of the proposed approach over other ...
SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation*... Y Chen,B Zou,Z Guo,... - IEEE 被引量: 0发表: 2023年 E2E-Swin-Unet++: An Enhanced End-to-End Swin-Unet Architecture With Dual Decoders For...
Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention. However, the features captured by CNN are always local that result in discontinuous fea...
CNN可以使用浅层模型和深层模型来提取良好的特征,这表明可以在不同级别上提取相关特征。尚未针对EEG数据对多个CNN模型进行融合。在这项工作中,我们提出了一种用于融合具有不同特性和架构的CNN的多层CNN方法,以提高EEG MI分类的准确性。我们的方法利用不同的卷积特征从原始EEG数据中捕获空间和时间特征。我们证明...
2.2 CNN-based methods Inspired by the success of deep learning in various vision tasks such as image classification [32], semantic segmentation [33,34] and object detection [35], a large number of CNN-based approaches have also been proposed to address crowd counting and achieve performance imp...
Most of the existing dehazing methods are based on learning and statistical priors. The convolutional neural network (CNN) is used in most learning-based d
To this end, a deep convolutional neural network (CNN) trained by high-quality image patches and their blurred versions is adopted to encode the mapping. The main novelty of this idea is that the activity level measurement and fusion rule can be jointly generated through learning a CNN model,...
更具体地说,对于 M3Net 中每一个模态的网络,我们首先制定一个并行多尺度网络的两个分支分别表示全局理解和局部捕获,由专门设计的 CNN 分支实现。全局理解分支和局部捕获分支的顶部分别采用一个完全连接的层和一个 1×1 卷积层。为每种模态所提出的多分支网络能够同时在全局范围内进行推理并捕获局部细节,而不是...
MATCNN utilizes the multi-scale fusion module (MSFM) to extract local features at different scales and employs the global feature extraction module (GFEM) to extract global features. Combining the two reduces the loss of detail features and improves the ability of global feature representation. ...