Therefore, a multi‐scale feature fusion pyramid attention network (PAN) for single image dehazing is proposed. In PAN, combined with the attention mechanism, a shallow and deep feature fusion (SDF) strategy is designed. SDF considers multi‐scale as well as channel‐level fusion to pro...
aggregated feature maps to the corresponding layers. Finally, the layered features are fused in a top-down fusion method to obtain the second Pred1 of the network. In the training phase, the optimized network parameters are obtained by deep supervision of the model. The process of the MFPF-N...
2. Although feature pyramids efficiently exploit features from all the layers in the network, they are not an attractive alternative to an image pyramid for detecting very small/large objects. 3. 多尺度图像分类 本节研究的是domain shift:训练和预测的输入是不同分辨率的图像。 我们之所以进行这种分析,...
For single image dehazing, an end-to-end multistage with multiattention network is proposed in this paper. The network contains two different stages, in wh... B Hu,M Gu,Y Li - 《Scientific Programming》 被引量: 0发表: 2022年 Pyramid Channel-based Feature Attention Network for image dehazin...
SSD [29] and MSCNN [2] predict objects at multiple layers of the network without merging features. Featurepyramid networks[26] extend the backbone model with a top-down pathway that gradually recovers featureresolutionfrom 1/32 to 1/4, using bilinear upsampling and lateral connection. The motiva...
Feature Pyramid Network PPV: positive predictive value NPV: negative predictive value AUC: area under the curve DCA: decision curve analysis TP: true positive TN: true negative FP: false positive FN: false negative ROC: receiver operating characteristic AUROC: area under ROC curves ...
Lin et al. proposed feature pyramids that are viewed as a basic component in recognition systems for detecting objects at different scales. A Feature Pyramid Network (FPN) with lateral connections has been developed for building high-level semantic feature maps at all scales and has been shown ...
Specifically, we present a consecutive multiscale feature-learning network (CMSFL-Net) that employs a consecutive feature-learning approach based on the usage of various feature maps with different receptive fields to achieve faster training/inference and higher accuracy. In the conducted experiments ...
作者提了Multi-Scale and Multi-Stream Deep Feature Learning,可以参考,但是感觉不全。 2. Method 主要创新点: 1)设计了Omni-Scale Residual Block,本质就是加权版多尺度特征融合,Inception升级版。 2)使用了Depthwise Separable Convolutions,成了lightweight network。
The problem of losing the key feature information of the small-scale target in the process of multiple downsampling is effectively avoided. Firstly, an enhanced multi-scale feature fusion pyramid network DSI-FPN is designed. The FPN+PAN network is optimized by using DepthwiseSparable ...