{"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/cross-modality-fusion-transformer-for","title":"Cross-Modality Fusion Transformer for Multispectral Object Detection","arxiv_id":"2111.00273","date":"2021-10-30","proceeding":null,"authors":["Fang Qingyun","Han Dapeng","Wang Zhaokui"],"abstract":"Multispectral image pairs can provide the combined information, making object detection applications more reliable and robust in the open world. To fully exploit the different modalities, we present a simple yet effective cross-modality feature fusion approach, named Cross-Modality Fusion Transformer (CFT) in this paper. Unlike prior CNNs-based works, guided by the transformer scheme, our network learns long-range dependencies and integrates global contextual information in the feature extraction stage. More importantly, by leveraging the self attention of the transformer, the network can naturally carry out simultaneous intra-modality and inter-modality fusion, and robustly capture the latent interactions between RGB and Thermal domains, thereby significantly improving the performance of multispectral object detection. Extensive experiments and ablation studies on multiple datasets demonstrate that our approach is effective and achieves state-of-the-art detection performance. Our code and models are available at https://github.com/DocF/multispectral-object-detection.","url_abs":"https://arxiv.org/abs/2111.00273v4","url_pdf":"https://arxiv.org/pdf/2111.00273v4.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":"cross-modality-fusion-transformer-for","repo_url":"https://github.com/docf/multispectral-object-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multispectral-object-detection","task_name":"Multispectral Object Detection"},{"task_slug":"object","task_name":"Object"},{"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":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multispectral-object-detection-on-flir-1","task":"Multispectral Object Detection","dataset":"FLIR","model":"CFT","rank_in_archive_order":9,"of":18,"metrics":{"mAP50":"77.7%"},"uses_additional_data":false},{"leaderboard":"/sota/multispectral-object-detection-on-flir-1","task":"Multispectral Object Detection","dataset":"FLIR","model":"YOLOv5 (T)","rank_in_archive_order":12,"of":18,"metrics":{"mAP50":"73.9%"},"uses_additional_data":false},{"leaderboard":"/sota/multispectral-object-detection-on-flir-1","task":"Multispectral Object Detection","dataset":"FLIR","model":"YOLOv5 (RGB)","rank_in_archive_order":17,"of":18,"metrics":{"mAP50":"67.8%"},"uses_additional_data":false},{"leaderboard":"/sota/multispectral-object-detection-on-llvip","task":"Multispectral Object Detection","dataset":"LLVIP","model":"CFT","rank_in_archive_order":1,"of":1,"metrics":{"mAP50":"97.5"},"uses_additional_data":false},{"leaderboard":"/sota/pedestrian-detection-on-cvc14","task":"Pedestrian Detection","dataset":"CVC14","model":"CFT","rank_in_archive_order":1,"of":2,"metrics":{"AP50":"78.2"},"uses_additional_data":false},{"leaderboard":"/sota/pedestrian-detection-on-dvtod","task":"Pedestrian Detection","dataset":"DVTOD","model":"CFT","rank_in_archive_order":2,"of":8,"metrics":{" mAP":"82.7"},"uses_additional_data":false},{"leaderboard":"/sota/pedestrian-detection-on-llvip","task":"Pedestrian Detection","dataset":"LLVIP","model":"CFT","rank_in_archive_order":4,"of":15,"metrics":{"AP":"0.636","log average miss rate":"5.40%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.00273","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}