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.

So Facebook AI's team came up with DeiT, which is a data-efficient transformer and was able to out-perform SOTA convolutional networks and ViTs, in terms of accuracy/FLOPs trade-off. DeiT was trained on no external data but just ImageNet21. But it used distillation and depended on a convolution network for knowledge distillation, so was not completely a convolution-free solution. Both DeiT and ViT, were just tested and designed for Image classification, with the general perception that, if a network architecture performs good for the image classification task, it is expected to do good on others because, "image classification is used as a benchmark for measuring the progress of a technique in the vision domain, any progress here translates to downstream tasks like detection and segmentation". There is no other work in my knowledge, that used ViT or DeiT as a feature extraction backbone, for tasks other than classification.