{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sharecmp-polarization-aware-rgb-p-semantic","title":"ShareCMP: Polarization-Aware RGB-P Semantic Segmentation","arxiv_id":"2312.03430","date":"2023-12-06","proceeding":null,"authors":["Zhuoyan Liu","Bo wang","Lizhi Wang","Chenyu Mao","Ye Li"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2312.03430v2","url_pdf":"https://arxiv.org/pdf/2312.03430v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sharecmp-polarization-aware-rgb-p-semantic","repo_url":"https://github.com/lefteyex/sharecmp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"uplight","name":"UPLight","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-mcubes","task":"Semantic Segmentation","dataset":"MCubeS","model":"ShareCMP (B2 RGB-A-D)","rank_in_archive_order":13,"of":22,"metrics":{"mIoU":"50.99%"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-mcubes","task":"Semantic Segmentation","dataset":"MCubeS","model":"ShareCMP(B2 RGB-D)","rank_in_archive_order":14,"of":22,"metrics":{"mIoU":"50.55"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-mcubes","task":"Semantic Segmentation","dataset":"MCubeS","model":"ShareCMP(B2 RGB-A)","rank_in_archive_order":16,"of":22,"metrics":{"mIoU":"50.34"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-mcubes-p","task":"Semantic Segmentation","dataset":"MCubeS (P)","model":"ShareCMP (B2 RGB-A-D)","rank_in_archive_order":3,"of":8,"metrics":{"mIoU":"50.99"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-mcubes-p","task":"Semantic Segmentation","dataset":"MCubeS (P)","model":"ShareCMP (B2 RGB-D)","rank_in_archive_order":4,"of":8,"metrics":{"mIoU":"50.55"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-mcubes-p","task":"Semantic Segmentation","dataset":"MCubeS (P)","model":"ShareCMP(B2 RGB-A)","rank_in_archive_order":6,"of":8,"metrics":{"mIoU":"50.34"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-uplight","task":"Semantic Segmentation","dataset":"UPLight","model":"ShareCMP (B2 RGB-FP)","rank_in_archive_order":1,"of":8,"metrics":{"mIoU":"92.45"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-zju-rgb-p","task":"Semantic Segmentation","dataset":"ZJU-RGB-P","model":"ShareCMP (B4 RGB-FP)","rank_in_archive_order":3,"of":13,"metrics":{"mIoU":"92.7"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-zju-rgb-p","task":"Semantic Segmentation","dataset":"ZJU-RGB-P","model":"ShareCMP (B2 RGB-FP)","rank_in_archive_order":5,"of":13,"metrics":{"mIoU":"92.4"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2312.03430","atlas_url":"https://app.syntology.ai/?focus=2312.03430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.03430"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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