When Simplicity Wins: Bottleneck-Aware Context Modeling for Lightweight Semantic Segmentation

2026-08-19

AI Summary

该研究针对高分辨率图像语义分割中的效率与准确性平衡问题，提出了一种轻量级框架SiConMo，通过瓶颈阶段的上下文建模来有效整合局部和全局信息。

Abstract

Semantic segmentation demands a careful balance between accuracy, efficiency, and scalability, which remains difficult to achieve for high-resolution imagery. Convolutional networks effectively model local patterns but struggle with long-range dependencies, whereas Vision Transformers capture global context at a high computational cost. While recent work largely focuses on encoder design, the bottleneck stage, central to contextual aggregation and information flow, has been relatively overlooked. We propose SiConMo, a lightweight yet effective framework, implemented in two variants: an RGB-only model (SiConMo) and a GME-enhanced variant (SiConMo$_\dagger$). We show that simplicity arises from a key design principle: at very low computational budgets, the bottleneck is the most efficient stage to integrate local and global context. SiConMo integrates three complementary components: a Token Pyramid Extraction Module for hierarchical multi-scale representation, a Transformer-Branched Depthwise Convolution block for bottleneck-aware context modeling, and a Feature Merging Module that preserves spatial structure while enhancing semantic consistency. Extensive experiments on ADE20K, PASCAL Context, Cityscapes, and COCO-Stuff demonstrate that SiConMo achieves a state-of-the-art accuracy-efficiency trade-off among lightweight semantic segmentation models, highlighting simplicity as a powerful design principle.

Problem

• semantic segmentation
• efficiency
• context modeling
• bottleneck stage
• lightweight models

Innovation

• bottleneck-aware context modeling
• lightweight semantic segmentation
• efficiency-accuracy trade-off