Rgb D Salient Object Detection On Stere
Métriques
Average MAE
S-Measure
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Average MAE | S-Measure |
|---|---|---|
| uc-net-uncertainty-inspired-rgb-d-saliency | 0.039 | 90.3 |
| is-depth-really-necessary-for-salient-object | 0.037 | 91.0 |
| cola-conditional-dropout-and-language-driven | 0.039 | 90.8 |
| bts-net-bi-directional-transfer-and-selection | 0.038 | 91.5 |
| uncertainty-inspired-rgb-d-saliency-detection | 0.039 | 89.8 |
| jl-dcf-joint-learning-and-densely-cooperative | 0.042 | 90.5 |
| bbs-net-rgb-d-salient-object-detection-with-a | 0.041 | 90.8 |
| siamese-network-for-rgb-d-salient-object | 0.039 | 91.1 |
| rethinking-rgb-d-salient-object-detection | 0.046 | 89.9 |
| contrast-prior-and-fluid-pyramid-integration | 0.051 | 87.9 |
| spsn-superpixel-prototype-sampling-network | 0.035 | 90.7 |
| dformer-rethinking-rgbd-representation | 0.030 | 92.3 |
| bilateral-attention-network-for-rgb-d-salient | 0.043 | 90.4 |
| uncertainty-inspired-rgb-d-saliency-detection | 0.037 | 90.4 |