# gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window Source: [gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window]() Collection index: [llms.txt]() Data updated: 2025-12-22T09:37:27.873Z Authors: Mocho Go, Hideyuki Tachibana Published: 2022-08-24T18:00:46.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()