We compare the different dilated rates of multiscale fusion block to obtain a better group of dilated rate as follows: (1) We first select (r1, r2, r3) = (1,2,3) with the maximum covenant of ri is not greater than 1 [11]. Next, we increase the multiple of ri based on the...
Specifically, the architecture includes structure feature extraction network (S-Net), detail information extraction network (D-Net) and multi-scale fusion block (MCBlock), which are in charge of extracting structural features, capturing texture details and merging features, respectively. In S-Net, ...
The residual module in RedNet is used as a basic building block in encoder and decoder to construct a fusion structure and propose a pyramid supervised training scheme. ACNet uses independent branches based on ResNet to fuse RGB features and depth features, and finally obtains segmentation results...
In this section, we describe the proposed method in detail including Res-block, Position-wise Attention Block and Multi-scale Fusion Attention Block. We adopt the improved encoder-decoder architecture of U-Net for liver and tumors segmentation in the paper. The Res-block consists of three 3×3...
Fig. 1: Overview of the rigid hierarchical fusion approach. Full size image Building block generation by rigid helical fusion of DHR arms to HB oligomers Fig. 2: Homo-oligomer diversification by repeat protein fusion. Full size image Higher order architectures with WORMS ...
This primarily involves a convolution block, depicted by the dashed line in Fig. 6a, which includes a 3 × 3 convolution, BatchNorm layer, and ReLU layer. This enhances the feature information of the two pathways to ensure a more comprehensive final output, ultimately improving the overall...
表中的E行是进行将 multi-scale transformer block重复6次,D行是只重复3次但是每次里面 cross-attention计算2次,结果表明计算效率更高的D行效果略好。作者的解释是:Patch token from the other branch is untouched, and the advantages from stacking more than one cross-attention is small as cross-attention ...
In this paper, for hyperspectral single-image super-resolution, we propose a multi-scale feature fusion and aggregation network with 3D convolution (MFFA-3D) by cascading the MFFA-3D block. The MFFA-3D block includes group multi-scale feature fusion part and multi-scale feature aggregation part....
the multi-scale feature fusion (MSF) strategy is utilized to fuse shallow and deep features effectively and enrich detailed information relevant to target defects. Secondly, the CSPLayer Res2Attention block (CRA block) residual module is introduced to reduce the loss of defect information during hie...
The dilated convolution block uses four parallel \(3 \times 3\) convolutions with the dilated rate of each layer set to 1, 3, 4, and 9, after which the four layers of features are connected through the \(1 \times 1\) convolution layer. The Transformer encoder module and the dilated ...