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Found 6 papers, 2 papers with code
HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation
In this paper, we introduce HAFormer, a model that combines the hierarchical features extraction ability of CNNs with the global dependency modeling capability of Transformers to tackle lightweight semantic segmentation challenges.
MFPNet: Multi-scale Feature Propagation Network For Lightweight Semantic Segmentation
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.
Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN
In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation.
FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation
Specifically, FBSNet employs a symmetrical encoder-decoder structure with two branches, semantic information branch and spatial detail branch.
MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation
In recent years, how to strike a good trade-off between accuracy and inference speed has become the core issue for real-time semantic segmentation applications, which plays a vital role in real-world scenarios such as autonomous driving systems and drones.
