MFPNet: Multi-scale Feature Propagation Network For Lightweight Semantic Segmentation

• URL: http://arxiv.org/abs/2309.04914v2
• Date: Tue, 12 Sep 2023 05:08:47 GMT
• Title: MFPNet: Multi-scale Feature Propagation Network For Lightweight Semantic Segmentation
• Authors: Guoan Xu, Wenjing Jia, Tao Wu, Ligeng Chen
• Abstract summary: We propose a novel lightweight segmentation architecture, called Multi-scale Feature Propagation Network (Net) We design a robust-Decoder structure featuring symmetrical residual blocks that consist of flexible bottleneck residual modules (BRMs) Taking benefit of their capacity to model latent long-range contextual relationships, we leverage Graph Convolutional Networks (GCNs) to facilitate multiscale feature propagation between the BRM blocks.
• Score: 5.58363644107113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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