Multi-scale representation ability is one of key criteria for measuring convolutional neural networks (CNNs) effectiveness. Recent studies have shown that multi-scale features can represent different semantic information of original images, and a combination of them would have positive influence on vision tasks. Many researchers are investigated in extract the multi-scale features in a layerwise manner and equipped with relatively inflexible receptive field. In this paper, we propose a multi-scale attention (MSA) module for CNNs, namely MSANet, where the residual block comprises hierarchical attention connections and skip connections. The MSANet improves the multi-scale representation power of the network by adaptively enriching the receptive fields of each convolutional branch. We insert the proposed MSANet block into several backbone CNN models and achieve consistent improvement over backbone models on CIFAR-100 dataset. To better verify the effectiveness of MSANet, the experimental results on major network details, i.e., scale, depth, further demonstrate the superiority of the MSANet over the Res2Net methods.
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MSANet: A Multi-Scale Attention Module
Semantic Scholar · Computer Science · 2019
Abstract
Multi-scale representation ability is one of key criteria for measuring convolutional neural networks (CNNs) effectiveness. Recent studies have shown that multi-scale features can represent different semantic information of original images, and a combination of them would have positive influence on vision tasks. Many researchers are investigated in extract the multi-scale features in a layerwise manner and equipped with relatively inflexible receptive field. In this paper, we propose a multi-scale attention (MSA) module for CNNs, namely MSANet, where the residual block comprises hierarchical attention connections and skip connections. The MSANet improves the multi-scale representation power of the network by adaptively enriching the receptive fields of each convolutional branch. We insert the proposed MSANet block into several backbone CNN models and achieve consistent improvement over backbone models on CIFAR-100 dataset. To better verify the effectiveness of MSANet, the experimental results on major network details, i.e., scale, depth, further demonstrate the superiority of the MSANet over the Res2Net methods.