Efficient Pyramid Multi-Scale Channel Attention Modules: To capture the fine-grained multi-scale local feature and establish the long-term dependencies between channels, an efficient pyramid-type multi-scale channel attention (EPMCA) module is proposed, as shown in Fig. 5. It first extracts the ...
The multi-scale Pyramid module inside the generator could extract the features containing high-frequency information, and then the high-resolution image with the results of the bicubic interpolations is reconstructed. The discriminator in our model is used to identify the authenticity of the input ...
In order to improve the detection accuracy of the network, it proposes multi-scale feature fusion and attention mechanism net (MFANet) based on deep learning, which integrates pyramid module and channel attention mechanism effectively. Pyramid module is designed for feature fusion in the channel and...
两种进一步创新的结构:KP-Pyramid 、RandLA-Pyramid 这一个模块就是为了掩饰提出的金字塔结构的encoder-decoder架构是通用的,故将其用在了另外两个网络中,并达到了好的效果。 Conclusion 论文提出了一种三向金字塔架构来处理和融合多尺度信息以进行点云分割。 论文使用了几个简单但有效的组件改进了常用的encoder-decoder...
Multi-scale text detectionGrouped pyramid moduleEfficient and effectiveScene text detection has attracted many researches due to its importance to various applications. However, current approaches could not keep a good balance between accuracy and speed, i.e., a high-performance accuracy but with a ...
level semantic information. Towards the end of the backbone, a Spatial Pyramid Pooling-Fast (SPPF) module is integrated. It establishes multi-branch, multi-scale pooling layers to create and amalgamate features of varied scales, thereby enhancing the network’s multi-scale feature representation ...
Specifically, the MSFD module first feeds the feature map outputs from MSFA module to the average pooling layers with pyramid down-sampling rates to convert the aggregated features to different scale spaces. As shown in Fig.1, the down-sampling rates are {8, 4, 2, 1, 1} from top to bo...
pyramid pooling (ASPP) module into the shallow network of ResNet101, designed to improve the multi-scale feature extraction capability of the model. The ASPP module enhances the robustness of recognition for different dimensional sizes and occluded keypoints using different dilatation rates in the ...
We introduce the problem definition, network structure, and pyramid attention module of multi-scale feature fusion in turn. Fig. 2 shows the complete structure of the network. We set the image Experiment In this section we first describe the dataset used in the experiments, then we introduce ...
Then, we expanded the scale of the module into a multi-scale detail enhanced module. The difference between central and peripheral information at different scales makes the network more sensitive to changes in details, resulting in more accurate segmentation. In order to reduce the impact of ...