Although traditional multi-scale detection networks have been successful in solving problems with such large variations, they still have certain limitations: (1) The traditional multi-scale detection methods note the scale of features but ignore the correlation between feature levels. Each feature map ...
Next, we use the CSFF module to obtain powerful and discriminative multilevel feature representations. Finally, we implement our work in the framework of Faster region-based CNN (R-CNN). In the experiment, we...
# Stage 1classCrossAttention(nn.Module):def__init__(self,dim,num_heads=8,qkv_bias=False,qk_scale=None):super(CrossAttention,self).__init__()assertdim%num_heads==0,f"dim{dim}should be divided by num_heads{num_heads}."self.dim=dimself.num_heads=num_headshead_dim=...
the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive ...
Multi-scale Feature Fusion Group 为获得精确的边缘信息,我们构建了CCB模块,见上图。除了Cross卷积外,CCB还包含F-Norm与CA(通道注意力,没什么可说的),两者分别用于空域与通道信息重要性挖掘。F-Norm可表示如下: 更多关于F-Norm的介绍可参考《Iterative Network for Image Super-resolution》一文,为方便理解,笔者在...
Multi-scale Feature Fusion Group 为获得精确的边缘信息,我们构建了CCB模块,见上图。除了Cross卷积外,CCB还包含F-Norm与CA(通道注意力,没什么可说的),两者分别用于空域与通道信息重要性挖掘。F-Norm可表示如下:F(i)out=(F(i)in⊗k(i)+b(i))+F(i)in 更多关于F-Norm的介绍可参考《Iterative Network fo...
And we embed cross-modal attention fusion modules in different scale feature extraction layers. As shown in Fig. 1, the cross-modal attention fusion network consists of the following four components. The dual branch encoder can extract RGB features and depth features. The Datasets and metrics ...
The Cross-Attention module is an attention module used in CrossViT for fusion of multi-scale features. The CLS token of the large branch (circle) serves as a query token to interact with the patch tokens from the small branch through attention. $f\left(
Feature fusion. This section describes the improvements to the convolutional layer mainly in terms of enhancing the capability of multi-scale feature fusion. In general, as shown in Fig. 2, the approaches of multi-scale feature fusion include the concatenation and the addition. The inception of ...
A new Multi-granularity Shared Feature Fusion (MSFF) network is proposed in this paper, which combines global and local features to learn different granularities representations of the two modalities, extracting multi-scale and multi-level features from the backbone network, where the coarse ...