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Transformers have been successfully used in various fields and are becomingthe standard tools in computer vision. However, self-attention, a corecomponent of transformers, has a quadratic complexity problem, which limits theuse of transformers in various vision tasks that require dense prediction. Manystudies aiming at solving this problem have been reported proposed. However, nocomparative study of these methods using the same scale has been reported dueto different model configurations, training schemes, and new methods. In ourpaper, we validate these efficient attention models on the ImageNet1Kclassification task by changing only the attention operation and examiningwhich efficient attention is better.

Fair Comparison between Efficient Attentions