Dichotomous Image Segmentation On Dis Vd

Métriques

E-measure

MAE

S-Measure

max F-Measure

weighted F-measure

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	E-measure	MAE	S-Measure	max F-Measure	weighted F-measure
ben-using-confidence-guided-matting-for	0.935	0.031	0.916	0.923	0.871
f3net-fusion-feedback-and-focus-for-salient	0.800	0.107	0.733	0.685	0.595
revisiting-image-pyramid-structure-for-high	-	-	0.900	0.889	-
patch-depth-fusion-dichotomous-image	0.944	0.030	0.916	0.913	0.873
pyramid-scene-parsing-network	0.802	0.102	0.744	0.691	0.603
multi-view-aggregation-network-for	0.941	0.034	0.905	0.904	0.863
global-context-aware-progressive-aggregation	0.765	0.118	0.718	0.648	0.542
highly-accurate-dichotomous-image	0.856	0.074	0.813	0.791	0.717
basnet-boundary-aware-salient-object	0.816	0.094	0.768	0.731	0.641
rethinking-atrous-convolution-for-semantic	0.796	0.114	0.716	0.660	0.568
concealed-object-detection	0.798	0.110	0.727	0.665	0.584
bilateral-reference-for-high-resolution	0.928	0.038	0.898	0.889	0.853
hyperseg-patch-wise-hypernetwork-for-real	0.814	0.096	0.773	0.734	0.640
u-net-convolutional-networks-for-biomedical	0.785	0.113	0.745	0.692	0.586
ben-using-confidence-guided-matting-for	0.958	0.027	0.917	0.919	0.896
icnet-for-real-time-semantic-segmentation-on	0.811	0.102	0.747	0.697	0.609
190807919	0.824	0.095	0.767	0.726	0.641
suppress-and-balance-a-simple-gated-network	0.783	0.110	0.723	0.678	0.574
rethinking-bisenet-for-real-time-semantic	0.817	0.103	0.740	0.696	0.613
revisiting-image-pyramid-structure-for-high	0.921	0.043	0.887	0.876	0.826
camouflaged-object-segmentation-with	0.811	0.106	0.740	0.691	0.604
bisenet-bilateral-segmentation-network-for	0.767	0.116	0.728	0.662	0.548
u-2-net-going-deeper-with-nested-u-structure	0.823	0.090	0.781	0.748	0.656
searching-for-mobilenetv3	0.841	0.092	0.758	0.714	0.642