  Multimodal semantic segmentation is developing rapidly, but the modality of
RGB-Polarization remains underexplored. To delve into this problem, we
construct a UPLight RGB-P segmentation benchmark with 12 typical underwater
semantic classes. In this work, we design the ShareCMP, an RGB-P semantic
segmentation framework with a shared dual-branch architecture, which reduces
the number of parameters by about 26-33% compared to previous dual-branch
models. It encompasses a Polarization Generate Attention (PGA) module designed
to generate polarization modal images with richer polarization properties for
the encoder. In addition, we introduce the Class Polarization-Aware Loss
(CPALoss) to improve the learning and understanding of the encoder for
polarization modal information and to optimize the PGA module. With extensive
experiments on a total of three RGB-P benchmarks, our ShareCMP achieves
state-of-the-art performance in mIoU with fewer parameters on the UPLight
(92.45(+0.32)%), ZJU (92.7(+0.1)%), and MCubeS (50.99(+1.51)%) datasets
compared to the previous best methods. The code is available at
https://github.com/LEFTeyex/ShareCMP.

Computer Vision and Pattern Recognition