Comments (4)
Thanks for your attention.
In my view, 1x1 kernel conv is equal to Linear Layer.
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Thanks. Are there multiple layers in MLP, or just one like the code?
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Great work! Would you like to share the speed of inference on Cityscapes with 1024*2048 resolution input? Or make some comparison with the SOTA real-time semantic segmentation work like DDRNet, BiSegNetv2, etc?
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Good suggestions.
I will try to integrate this block into real-time semantic segmentation framework in the future.
Thanks for your kind advice.
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Related Issues (20)
- 点云的代码会发布吗? HOT 1
- Reproducing semantic segmentation result on PASCAL VOC HOT 1
- loss损失函数不降低 HOT 1
- 关于 model_torch.py中的问题 HOT 1
- Question about multi-head EA HOT 2
- MACs calculation HOT 1
- could we use conv2d to instead conv1d in ea module? HOT 1
- The problem of the program running on Cifar10/100 HOT 1
- No Resnet connection ?
- External Attention v.s. Covolutional Kernel HOT 1
- multi_head_attention_torch.py
- External_attention function(model_torch.py)
- Visualization Code of Attention Maps
- 消融实验 HOT 1
- 分类模型是用8卡跑的吗? HOT 1
- the code of multi head external attetion can't work HOT 3
- EA代码细节问题
- Resnet pretrained pth?
- External_attention 在 t2t_vit 中何处被使用?
- ModuleNotFoundError: No module named 'bn_lib' HOT 1
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