Yixin Guo , Zhenxue Chen , Xuewen Rong , Chengyun Liu , Lili Song , Yidi Li
{"title":"RGB-T语义切分的跨模态协同校正网络","authors":"Yixin Guo , Zhenxue Chen , Xuewen Rong , Chengyun Liu , Lili Song , Yidi Li","doi":"10.1016/j.imavis.2025.105638","DOIUrl":null,"url":null,"abstract":"<div><div>Utilizing RGB-Thermal (RGB-T) data, multi-modal semantic segmentation enables pixel-wise classification of images across diverse environmental conditions, including variations in lighting and occlusions. However, a significant challenge persists in reducing the discrepancies between modalities while effectively utilizing their complementary strengths. To address this issue, a novel Cross-modal Cooperative Correction Network (3CNet) is proposed for RGB-T semantic segmentation. The core of 3CNet lies in the “correction-then-fusion” strategy. The cross-modal cooperative correction module employs orthogonal and spatial attention mechanisms to rectify the feature representations, thereby ensuring consistency and reliability of features for subsequent fusion. Additionally, a unique triple-stream feature fusion structure is introduced to enhance the efficiency of multi-modal feature utilization and improve overall fusion performance. Our proposed network demonstrates state-of-the-art performance on two RGB-T datasets, highlighting the potential of multi-modal information in advancing segmentation accuracy and confirming its efficacy in practical applications. The code and results are available at <span><span>https://github.com/GraceGuoo/3CNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"161 ","pages":"Article 105638"},"PeriodicalIF":4.2000,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"3CNet: Cross-modal cooperative correction network for RGB-T semantic segmentation\",\"authors\":\"Yixin Guo , Zhenxue Chen , Xuewen Rong , Chengyun Liu , Lili Song , Yidi Li\",\"doi\":\"10.1016/j.imavis.2025.105638\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Utilizing RGB-Thermal (RGB-T) data, multi-modal semantic segmentation enables pixel-wise classification of images across diverse environmental conditions, including variations in lighting and occlusions. However, a significant challenge persists in reducing the discrepancies between modalities while effectively utilizing their complementary strengths. To address this issue, a novel Cross-modal Cooperative Correction Network (3CNet) is proposed for RGB-T semantic segmentation. The core of 3CNet lies in the “correction-then-fusion” strategy. The cross-modal cooperative correction module employs orthogonal and spatial attention mechanisms to rectify the feature representations, thereby ensuring consistency and reliability of features for subsequent fusion. Additionally, a unique triple-stream feature fusion structure is introduced to enhance the efficiency of multi-modal feature utilization and improve overall fusion performance. Our proposed network demonstrates state-of-the-art performance on two RGB-T datasets, highlighting the potential of multi-modal information in advancing segmentation accuracy and confirming its efficacy in practical applications. The code and results are available at <span><span>https://github.com/GraceGuoo/3CNet</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":50374,\"journal\":{\"name\":\"Image and Vision Computing\",\"volume\":\"161 \",\"pages\":\"Article 105638\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2025-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Image and Vision Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0262885625002264\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885625002264","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
3CNet: Cross-modal cooperative correction network for RGB-T semantic segmentation
Utilizing RGB-Thermal (RGB-T) data, multi-modal semantic segmentation enables pixel-wise classification of images across diverse environmental conditions, including variations in lighting and occlusions. However, a significant challenge persists in reducing the discrepancies between modalities while effectively utilizing their complementary strengths. To address this issue, a novel Cross-modal Cooperative Correction Network (3CNet) is proposed for RGB-T semantic segmentation. The core of 3CNet lies in the “correction-then-fusion” strategy. The cross-modal cooperative correction module employs orthogonal and spatial attention mechanisms to rectify the feature representations, thereby ensuring consistency and reliability of features for subsequent fusion. Additionally, a unique triple-stream feature fusion structure is introduced to enhance the efficiency of multi-modal feature utilization and improve overall fusion performance. Our proposed network demonstrates state-of-the-art performance on two RGB-T datasets, highlighting the potential of multi-modal information in advancing segmentation accuracy and confirming its efficacy in practical applications. The code and results are available at https://github.com/GraceGuoo/3CNet.
期刊介绍:
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.