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This research introduces AMSRA-Net, addressing limitations in CNNs for image classification.

AMSRA-Net: An Adaptive Multi-Scale Residual Attention Network for Image Classification

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Adaptive multi-scale residual attention network improves classification accuracy in image tasks, suggesting novel CNN architectures.

This research introduces AMSRA-Net, addressing limitations in CNNs for image classification.
Developed an Adaptive Multi-Scale Residual Attention Network architecture.
Integrated multi-scale features with global self-attention.
Conducted experiments on the CIFAR-10 dataset to evaluate performance.
Achieved 95.89% classification accuracy, surpassing ResNet-18 and Compact Convolutional Transformers.
Showed significant drops in accuracy without the attention mechanism or using single-scale convolution.

Jiang et al. (2026) studied this question.

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