  In contrast to the abundant research focusing on large-scale models, the
progress in lightweight semantic segmentation appears to be advancing at a
comparatively slower pace. However, existing compact methods often suffer from
limited feature representation capability due to the shallowness of their
networks. In this paper, we propose a novel lightweight segmentation
architecture, called Multi-scale Feature Propagation Network (MFPNet), to
address the dilemma. Specifically, we design a robust Encoder-Decoder structure
featuring symmetrical residual blocks that consist of flexible bottleneck
residual modules (BRMs) to explore deep and rich muti-scale semantic context.
Furthermore, taking benefit from their capacity to model latent long-range
contextual relationships, we leverage Graph Convolutional Networks (GCNs) to
facilitate multi-scale feature propagation between the BRM blocks. When
evaluated on benchmark datasets, our proposed approach shows superior
segmentation results.
