{"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/cacfnet-cross-modal-attention-cascaded-fusion","title":"CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing","arxiv_id":null,"date":"2023-09-14","proceeding":"journal 2023 9","authors":["WuJie Zhou","Shaohua Dong","Meixin Fang","Lu Yu"],"abstract":"Color–thermal (RGB-T) urban scene parsing has recently attracted widespread interest. However, most existing approaches to RGB-T urban scene parsing do not deeply explore the information complementarity between RGB-T features. In this study, we propose a cross-modal attention-cascaded fusion network (CACFNet) that fully exploits cross-modality. In our design, a cross-modal attention fusion module mines complementary information from two modalities. Subsequently, a cascaded fusion module decodes the multi-level features in an up-bottom manner. Noting that each pixel is labeled with the category of the region to which it belongs, we present a region-based module that explores the relationship between pixel and region. Moreover, in contrast to previous methods that employ only the cross-entropy loss to penalize pixel-wise predictions, we propose an additional loss to learn pixel–pixel relationships. Extensive experiments on two datasets demonstrate that the proposed CACFNet achieves state-of-the-art performance in RGB-T urban scene parsing","url_abs":"https://ieeexplore.ieee.org/abstract/document/10251592","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10251592","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":[],"tasks":[{"task_slug":"scene-parsing","task_name":"Scene Parsing"},{"task_slug":"thermal-image-segmentation","task_name":"Thermal Image Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/thermal-image-segmentation-on-mfn-dataset","task":"Thermal Image Segmentation","dataset":"MFN Dataset","model":"CACFNet","rank_in_archive_order":18,"of":55,"metrics":{"mIOU":"57.8"},"uses_additional_data":false},{"leaderboard":"/sota/thermal-image-segmentation-on-pst900","task":"Thermal Image Segmentation","dataset":"PST900","model":"CACFNet","rank_in_archive_order":7,"of":22,"metrics":{"mIoU":"86.56"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}