  Semantic segmentation usually benefits from global contexts, fine
localisation information, multi-scale features, etc. To advance
Transformer-based segmenters with these aspects, we present a simple yet
powerful semantic segmentation architecture, termed as IncepFormer. IncepFormer
has two critical contributions as following. First, it introduces a novel
pyramid structured Transformer encoder which harvests global context and fine
localisation features simultaneously. These features are concatenated and fed
into a convolution layer for final per-pixel prediction. Second, IncepFormer
integrates an Inception-like architecture with depth-wise convolutions, and a
light-weight feed-forward module in each self-attention layer, efficiently
obtaining rich local multi-scale object features. Extensive experiments on five
benchmarks show that our IncepFormer is superior to state-of-the-art methods in
both accuracy and speed, e.g., 1) our IncepFormer-S achieves 47.7% mIoU on
ADE20K which outperforms the existing best method by 1% while only costs half
parameters and fewer FLOPs. 2) Our IncepFormer-B finally achieves 82.0% mIoU on
Cityscapes dataset with 39.6M parameters. Code is
available:github.com/shendu0321/IncepFormer.
