Camouflaged Object Segmentation On Pcod 1200
Métriques
S-Measure
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | S-Measure |
|---|---|
| camouflaged-object-detection-via-context | 0.895 |
| pranet-parallel-reverse-attention-network-for | 0.904 |
| cmx-cross-modal-fusion-for-rgb-x-semantic | 0.922 |
| fast-camouflaged-object-detection-via-edge | 0.833 |
| glass-segmentation-using-intensity-and | 0.916 |
| concealed-object-detection | 0.882 |
| cascaded-partial-decoder-for-fast-and | 0.855 |
| camouflaged-object-detection | 0.862 |
| f3net-fusion-feedback-and-focus-for-salient | 0.885 |
| simultaneously-localize-segment-and-rank-the | 0.888 |
| egnet-edge-guidance-network-for-salient | 0.861 |
| zoom-in-and-out-a-mixed-scale-triplet-network | 0.897 |
| basnet-boundary-aware-salient-object | 0.837 |
| camouflaged-object-segmentation-with | 0.873 |
| unet-a-nested-u-net-architecture-for-medical | 0.801 |
| context-aware-cross-level-fusion-network-for | 0.893 |