Papers › CAFF-DINO: Multi-spectral object detection transformers with cross-attention features fusion
CAFF-DINO: Multi-spectral object detection transformers with cross-attention features fusion
Kevin Helvig, Baptiste Abeloos, Pauline Trouve-Peloux
Object detection on images can find benefit from coupling multiple spectra, each presenting specific useful features. However, building an efficient architecture coupling the different modalities is a complex task. Transformers, due to their ability to extract meaningful correlations between the different regions of the inputs appear as a promising way to perform features fusion across different spectra. This work presents a multi-spectral object detection architecture based on cross-attention features fusion (CAFF), combined with a transformer based detector (DINO). We demonstrate here the performance of the proposed approach in object detection compared with state-of-the-art approaches, on infrared-visible multi-spectral datasets. Moreover the robustness to systematic misalignment between image pairs is studied. The proposed approach is generic to any mono-spectrum transformer based detectors. The model developed in this study will be available in a dedicated github repository.
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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 | CAFF-DINO | mAP | 50.5% | #3 of 18 | Archive leaderboard | report |
| Multispectral Object Detection | FLIR | CAFF-DINO | mAP50 | 85.5% | #3 of 18 | Archive leaderboard | report |
| Pedestrian Detection | LLVIP | CAFF-DINO | AP | 0.685 | #2 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.
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