Research map / Architectures and efficiency / CNN design
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add SimAM (parameter-free 3-D attention) | ResNet-20/56/110, PreResNet, WideResNet-20x10, MobileNetV2 · CIFAR-10 and CIFAR-100 | top-1 accuracy | Helped |
| add SimAM | ResNet-18/34/50/101, ResNeXt-50, MobileNetV2 · ImageNet-1k | top-1 accuracy: ResNet-18 +0.98; ResNet-50 +1.11 | Helped |
| add SimAM | ResNet-50 and ResNet-101 in Faster/Mask R-CNN · COCO detection | AP: Faster RCNN ResNet-50 +1.4 AP | Helped |
| add SimAM | ResNet-50 with Faster R-CNN and ResNet-101 MobileNetV2 etc. · ImageNet-1k vs SE | top-1 accuracy | Mixed |
| add SimAM vs SE on COCO instance segmentation | ResNet-50/101 Mask R-CNN · COCO instance segmentation | mask AP | No clear effect |
| lambda sensitivity: lambda from 1e-1 to 1e-6 (chosen 1e-4 on CIFAR) (ablation) | ResNet-20 with SimAM · CIFAR-100 train-split cross validation | top-1 accuracy | No clear effect |
Closest papers by meaning
- Learn to Pay Attention (ICLR 2018 · similarity 0.90)
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- EAN: An Efficient Attention Module Guided by Normalization for Deep Neural Networks (AAAI 2024 · similarity 0.88)
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- AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation (NeurIPS 2024 · similarity 0.87)
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