TL;DR
CMT-UNet, a CNN-Mamba-Transformer hybrid architecture built on an architecture-level principle for efficient global–local coupling in dense prediction, achieves favorable accuracy–efficiency trade-offs against mainstream CNN-, Transformer-, and Mamba-based alternatives.
MCMB-UNet is proposed, an effective dual-path encoder model with multi-attention mechanisms that achieves a favorable accuracy-efficiency trade-off compared to mainstream Transformer-based models, and demonstrates good applicability on the Inria Aerial Labeling and Massachusetts Buildings datasets.
The accurate segmentation of remote sensing imagery is critical for precision agriculture but challenging due to spectral complexity and ambiguous interclass boundaries. The convolutional neural networks are limited in modeling global context, while transformer-based methods incur high computational overhead. This lett...
The results indicate that the collaboration between global semantics and local details within a unified weighting domain can effectively improve the separability and deploy ability of high-resolution segmentation.
Precise segmentation of small and dense field crop pests in UAV images remains challenging due to limited long-range dependency modeling in CNNs and quadratic complexity in ViTs. Although VM-UNet captures global context efficiently, it lacks local texture detail and multi-scale feature integration for small targets. To...
Experimental results on the ISPRS Potsdam and Vaihingen benchmark datasets show that the proposed CNN-Transformer model attains highly competitive segmentation performance while maintaining computational efficiency.
This study proposes RFM-UNet, a hybrid frequency and state–space network designed for road-network segmentation that consistently outperforms current state-of-the-art methods.
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