Multispectral Object Detection On Kaist
Métriques
All Miss Rate
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | All Miss Rate |
|---|---|
| cian-cross-image-affinity-net-for-weakly | 35.53 |
| mlpd-multi-label-pedestrian-detector-in | - |
| illumination-aware-faster-r-cnn-for-robust | 44.23 |
| translation-scale-and-rotation-cross-modal | 30.74 |
| multispectral-pedestrian-detection-via | 34.15 |
| confidence-aware-fusion-using-dempster-shafer | 28.98 |
| the-cross-modality-disparity-problem-in | 34.95 |
| unirgb-ir-a-unified-framework-for-visible | 25.21 |
| multispectral-fusion-for-object-detection | - |
| improving-multispectral-pedestrian-detection | 31.87 |
| fully-convolutional-networks-for-semantic-1 | 51.70 |
| fusion-of-multispectral-data-through | 48.96 |
| removal-and-selection-improving-rgb-infrared | 24.79 |
| insanet-intra-inter-spectral-attention | - |
| mathbf-c-2-former-calibrated-and | 28.39 |
| multispectral-deep-neural-networks-for | 49.18 |
| guided-attentive-feature-fusion-for | - |