Comments (5)
Thanks for your attention to our work. "AP_level =2" denotes that the attention pyramid has both level-1 attention and level-2 attention. It is worth noting that the level-1 attention is the baseline global attention model (e.g. SE attention in the channel-wise model). Only level-2 attention needs a SAMS module to split and merge. It is consistent with our paper (e.g. we write “APNet-C1 only has a global attention map“on page 8). Please see the "Ablation Studies" part for more details.
Please do not hesitate to contact me if you have any additional questions.
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Thanks for your reply. I noticed that APNet-C1 slightly outperforms SE-ResNet accodding to TABLE 2 in your paper. It was a little unexpected for me, since APNet-C1 has quite fewer SELayer with different location(after f(x) + x). What do you think about this, please?
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Personally, I think APNet-C1 and SE-ResNet are comparable. It may be because the number of attention layers in APNet-C1 is enough for Person ReID (Market and Duke). In our experiments, we don't find a consistent law between the number of attention layers and ReID performance.
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It seems that SELayer takes no effects. Please see
APNet/modeling/backbones/se_resnet.py
Line 76 in 74efb12
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Thank you very much for pointing out this!We have fixed this. It is a typo that occurs when we cleaned the code and released it for git. We only checked the APNet but ignored to check this. This typo has no influence on the conclusions in the paper because it is only in this repo but not in our experiments. Thank you again for helping us find this typo. Please let me know if you have any other questions.
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Related Issues (11)
- APNet算法中cuhk03测试结果问题 HOT 4
- accuracy HOT 1
- The map of the datasets MSMT is different from that in the paper HOT 4
- Generate a test image that corresponds to that part of the code. HOT 6
- The code in APNet/modeling/baseline.py does not match the description in Figure 2 HOT 5
- SAM part of the code HOT 1
- SAM fig2 HOT 2
- 关于APlevel=3时报错 HOT 1
- the code of spatial attention HOT 1
- 测试时加载模型 HOT 1
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