  Following the success in language domain, the self-attention mechanism
(transformer) is adopted in the vision domain and achieving great success
recently. Additionally, as another stream, multi-layer perceptron (MLP) is also
explored in the vision domain. These architectures, other than traditional
CNNs, have been attracting attention recently, and many methods have been
proposed. As one that combines parameter efficiency and performance with
locality and hierarchy in image recognition, we propose gSwin, which merges the
two streams; Swin Transformer and (multi-head) gMLP. We showed that our gSwin
can achieve better accuracy on three vision tasks, image classification, object
detection and semantic segmentation, than Swin Transformer, with smaller model
size.
