AINeutralarXiv – CS AI · Mar 34/106
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MixerCSeg: An Efficient Mixer Architecture for Crack Segmentation via Decoupled Mamba Attention
Researchers have developed MixerCSeg, a new AI architecture for crack segmentation that combines CNN, Transformer, and Mamba-based approaches to achieve state-of-the-art performance with high efficiency. The model uses only 2.05 GFLOPs and 2.54M parameters while outperforming existing methods on crack detection benchmarks.