  Semantic segmentation is an important task for numerous applications but it
is still quite challenging to achieve advanced performance with limited
computational costs. In this paper, we present CGRSeg, an efficient yet
competitive segmentation framework based on context-guided spatial feature
reconstruction. A Rectangular Self-Calibration Module is carefully designed for
spatial feature reconstruction and pyramid context extraction. It captures the
axial global context in both horizontal and vertical directions to explicitly
model rectangular key areas. A shape self-calibration function is designed to
make the key areas closer to foreground objects. Besides, a lightweight Dynamic
Prototype Guided head is proposed to improve the classification of foreground
objects by explicit class embedding. Our CGRSeg is extensively evaluated on
ADE20K, COCO-Stuff, and Pascal Context benchmarks, and achieves
state-of-the-art semantic performance. Specifically, it achieves $43.6\%$ mIoU
on ADE20K with only $4.0$ GFLOPs, which is $0.9\%$ and $2.5\%$ mIoU better than
SeaFormer and SegNeXt but with about $38.0\%$ fewer GFLOPs. Code is available
at https://github.com/nizhenliang/CGRSeg.
