{"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/guided-attentive-feature-fusion-for","title":"Guided Attentive Feature Fusion for Multispectral Pedestrian Detection","arxiv_id":null,"date":"2021-01-03","proceeding":"WACV 2021 1","authors":["Heng Zhang","Elisa Fromont","Sebastien Lefevre","Bruno AVIGNON3"],"abstract":"Multispectral image pairs can provide complementary\r\nvisual information, making pedestrian detection systems\r\nmore robust and reliable. To benefit from both RGB and\r\nthermal IR modalities, we introduce a novel attentive multispectral feature fusion approach. Under the guidance of\r\nthe inter- and intra-modality attention modules, our deep\r\nlearning architecture learns to dynamically weigh and fuse\r\nthe multispectral features. Experiments on two public multispectral object detection datasets demonstrate that the proposed approach significantly improves the detection accuracy at a low computation cost.","url_abs":"https://openaccess.thecvf.com/content/WACV2021/papers/Zhang_Guided_Attentive_Feature_Fusion_for_Multispectral_Pedestrian_Detection_WACV_2021_paper.pdf","url_pdf":"https://openaccess.thecvf.com/content/WACV2021/papers/Zhang_Guided_Attentive_Feature_Fusion_for_Multispectral_Pedestrian_Detection_WACV_2021_paper.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":"guided-attentive-feature-fusion-for","repo_url":"https://github.com/ZHANGHeng19931123/GAFF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"multispectral-object-detection","task_name":"Multispectral Object Detection"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multispectral-object-detection-on-flir-1","task":"Multispectral Object Detection","dataset":"FLIR","model":"GAFF (ResNet18)","rank_in_archive_order":13,"of":18,"metrics":{"mAP50":"72.9%"},"uses_additional_data":false},{"leaderboard":"/sota/multispectral-object-detection-on-flir-1","task":"Multispectral Object Detection","dataset":"FLIR","model":"GAFF (VGG16)","rank_in_archive_order":14,"of":18,"metrics":{"mAP50":"72.7%"},"uses_additional_data":false},{"leaderboard":"/sota/multispectral-object-detection-on-kaist","task":"Multispectral Object Detection","dataset":"KAIST Multispectral Pedestrian Detection Benchmark","model":"GAFF","rank_in_archive_order":17,"of":17,"metrics":{"Reasonable Miss Rate":"6.48"},"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}