Introduce the Result Into Self-Attention
This is an incremental improvement for image classification tasks, specifically enhancing attention mechanisms in convolutional networks.
The paper tackles the limitation of traditional self-attention mechanisms by introducing a method that incorporates the classification network's output into the attention network, using an auxiliary classifier from GoogLeNet. This approach, when added to SE-ResNet, achieved a classification accuracy improvement of up to 1.94% on CIFAR-100.
Traditional self-attention mechanisms in convolutional networks tend to use only the output of the previous layer as input to the attention network, such as SENet, CBAM, etc. In this paper, we propose a new attention modification method that tries to get the output of the classification network in advance and use it as a part of the input of the attention network. We used the auxiliary classifier proposed in GoogLeNet to obtain the results in advance and pass them into attention networks. we added this mechanism to SE-ResNet for our experiments and achieved a classification accuracy improvement of at most 1.94% on cifar100.