Comments (1)
您好,
1.代码最开始设计是使用SEblock,但是考虑到部分用户在使用过程中出现报错(具体报错有点忘记),所以改成了乘以系数
2.可以理解成卷积是一个提取空间注意力的attention map的作用,相乘即可获取内部空间信息。
3.这里论文中有错误,应该改为3X3,感谢指正。
4.本文章由于其中一个出发点是 尽量减少参数量。所以在实验设计时只考虑像素级融合和浅层特征融合。您说的RGB-T的论文大部分都是基于two-stream结构的特征级融合一般处于比较深度的中间特征层,所以提取到的特征信息可能会比浅层的更具备语义信息,效果一般是较好的。
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