港大开源新型语义分割框架SegMAN：Mamba和局部自注意力首次融进视觉主干

TL;DR - HKU researchers introduced SegMAN, an open-source semantic segmentation framework combining Mamba-based global modeling with local self-attention and multiscale decoding. It improves segmentation accuracy and inference efficiency across major benchmarks while using fewer parameters than several larger vision backbones.

• The SegMAN Encoder’s LASS module combines sliding-window neighborhood attention for fine details with SS2D state-space scanning for long-range dependencies at linear complexity.
• Its Mamba-based MMSCopE decoder aggregates multiresolution features and propagates global semantics across scales.
• SegMAN-B’s 45M-parameter encoder reportedly outperforms larger VMamba-B and ConvNeXt-B backbones on ImageNet-1K.
• On 2048×1024 Cityscapes images, SegMAN-T achieves roughly three times EDAFormer-T’s frame rate while improving mIoU by 1.6 percentage points.