Our Global Attention Upsample module performs global average pooling to provide global context as a ...
Our Global Attention Upsample module performs global average pooling to provide global context as a ...
With our proposed augmented global attention convolution, our model can capture global information. Mei et al. [21] proposed a well-designed non-local attention module to extract cross-scale similarities and achieved promising results. However, their model lacks position information. Position ...
It progressively upsamples the top side output of EM and fuses the resulted upsampled features with the side outputs of EM at each resolution level. Such fused features combine the global context information in shallow layers and the semantic information in deep layers for global refinement. ...
We first design two branches using a parallel residual mixer (PRM) module and dilate convolution block to capture the local and global information of the image. At the same time, a SE-Block and a new spatial attention module enhance the output features. Considering the different output features...
Thereafter, a pyramid pooling module is used to capture the multiscale global context features and fuse them to achieve stronger feature representation. The decoder network upsamples the fused features to the same size as the input image, combining the high-resolution features with the contracting ...
In another word, we use the upsample network to transform the convolutional features into attention maps along the temporal direction, as shown in Fig. 2. In more detail, the upsample network is composed of five groups of upsample module, and each upsample module consists of three transposed ...
Global pooling之后可以得到chanel维度数量的特征向量，与一般的pooling完接激活层再接全连接层相比，可以...
The applicability of the E-OA module was validated on three state-of-the-art baseline models, and it was compared with advanced attention mechanisms. Experiments were conducted and analyzed on two datasets. In summary, this paper makes the following contributions: (1) We developed the E-OA, ...
Corresponding to the five-level cross-modal cross-scale fusion, the cascaded decoder is a five-level module that consists of five decoders, as shown in Figure 1. Each decoder contains 3×33×3 convolution layers and upsamples layers. Finally, we obtained the final prediction map 𝑆1S1, ...