Papers › Cross-Modality Fusion Transformer for Multispectral Object Detection
Cross-Modality Fusion Transformer for Multispectral Object Detection
Fang Qingyun, Han Dapeng, Wang Zhaokui
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multispectral Object Detection | FLIR | CFT | mAP50 | 77.7% | #9 of 18 | Archive leaderboard | report |
| Multispectral Object Detection | FLIR | YOLOv5 (T) | mAP50 | 73.9% | #12 of 18 | Archive leaderboard | report |
| Multispectral Object Detection | FLIR | YOLOv5 (RGB) | mAP50 | 67.8% | #17 of 18 | Archive leaderboard | report |
| Multispectral Object Detection | LLVIP | CFT | mAP50 | 97.5 | #1 of 1 | Archive leaderboard | report |
| Pedestrian Detection | CVC14 | CFT | AP50 | 78.2 | #1 of 2 | Archive leaderboard | report |
| Pedestrian Detection | DVTOD | CFT | mAP | 82.7 | #2 of 8 | Archive leaderboard | report |
| Pedestrian Detection | LLVIP | CFT | AP | 0.636 | #4 of 15 | Archive leaderboard | report |
| Pedestrian Detection | LLVIP | CFT | log average miss rate | 5.40% | #4 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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