Research Article
Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture
Table 1
The proposed AMU-Net model configurations.
| Layer | Kernel | Stride | Channel | Output size |
| Input | — | — | 3 | | conv1_1 | | 1 | 64 | | conv1_2 | | 1 | 64 | | maxpool_1 | | 2 | 64 | | conv2_1 | | 1 | 128 | | conv2_2 | | 1 | 128 | | maxpool_2 | | 2 | 128 | | conv3_1 | | 1 | 256 | | conv3_2 | | 1 | 256 | | conv3_3 | | 1 | 256 | | maxpool_3 | | 2 | 256 | | conv4_1 | | 1 | 512 | | conv4_2 | | 1 | 512 | | conv4_3 | | 1 | 512 | | maxpool_4 | | 2 | 512 | | conv5_1 | | 1 | 512 | | conv5_2 | | 1 | 512 | | conv5_3 | | 1 | 512 | | conv6 | | 1 | 512 | | attention4 | | 1 | 512 | | attention3 | | 1 | 256 | | attention2 | | 1 | 128 | | attention1 | | 1 | 64 | | tranconv4 | | 2 | 256 | | conv4d | | 1 | 512 | | tranconv3 | | 2 | 256 | | conv3d | | 1 | 256 | | tranconv2 | | 2 | 128 | | conv2d | | 1 | 128 | | tranconv1 | | 2 | 64 | | conv1d | | 1 | 64 | | conv_out | | 1 | 1 | |
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