SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Neural Information Processing Systems 

SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding, thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution differs from training.