以ResNet50为例,测试加在不同stage下的结果。可以看出在res2,3,4部分得到的结果相对baseline提升比较大,但是res5就一般了,这有可能是由于第5个stage中的feature map的spatial size比较小,信息比较少,所以提升比较小。 尝试添加不同数量的non local block ,结果如下。可以发现,添加越多的non local 模块,其效果越...
defforward(self,x):#[N,C,1,1]context=self.spatial_pool(x)out=xifself.channel_mul_conv is not None:#[N,C,1,1]channel_mul_term=torch.sigmoid(self.channel_mul_conv(context))out=out*channel_mul_termifself.channel_add_conv is not None:#[N,C,1,1]channel_add_term=self.channel_add...
【论文阅读】Further Non-local and Channel Attention Networks for Vehicle Re-identification visual cortex 提出了一种有效的注意力融合方法 ,充分模拟了空间注意力和信道注意力的影响。 Proposed method Then, we change the last spatial...问题: 类间差异小,类内差异大 提出:双分支自适应注意网络在视觉皮层双流...
As shown in Fig.4, we propose a spatial attention module (SAM) to enhance the spatial information of the output of the features by branch G to integrate the global contextual information. Motivated by8,42,43, SAM converts the input features into Q, V, K features. Matrix multiplication is...
In order to strike a balance between performance and complexity, this paper proposes a lightweight Mixed Local Channel Attention (MLCA) module to improve the performance of the object detection network, and it can simultaneously incorporate both channel information and spatial information, as well as...
Firstly, leveraging the spatial inhibition mechanism from neuroscience theory as a foundation, an energy function for the local feature and spatial attention mechanism is formulated. Subsequently, by employing optimization theory, a closed-form solution for the energy function is derived, which ensures ...
2⃣只对spatial操作,可以叠加上channel的信息。 四、Criss-Cross attention 1. 动机 non-local可以在feature map的空间上可以捕捉长距离的上下文信息,但是中间过程的相关性矩阵大小为WH✖WH,当输入的feature size过大时,会占用非常多的显存(亲身经历,平时只要2-3G的显存,加入non-local后占用超8G)。1⃣作者在...
On the one hand, the module learns the spatial structure of key points in a local region through local attention. On the other hand, it helps the model adapt to feature changes at different scales through multi-scale interaction. In addition, we employed DA on the training method to ...
总的来说,DANet网络主要思想是CBAM 和 non-local 的融合变形。把deep feature map进行spatial-wise self-attention,同时也进行channel-wise self-attetnion,最后将两个结果进行 element-wise sum 融合。 在CBAM 分别进行空间和通道 self-attention的思想上,直接使用了 non-local 的自相关矩阵 Matmul 的形式进行运算...
(self-attention). LHC is based on two main ideas: first, we think that in computer vision the best way to leverage the self-attention paradigm is the channel-wise application instead of the more explored spatial attention and that convolution will not be replaced by attention modules like ...