Awesome

TrasformerSOD

Difficulty-aware Salient Object Detection via Deeply-supervised Transformer.

A unified framework for Trasformer supervised/weakly supervised SOD, RGBD SOD, COD.

Code will be released very soon.

The saliency maps of DUTS, ECSSD, DUT, HKU-IS, PASCAL-S, SOD dataset in fully supervised manner are as: saliency maps1

	S_measure	F_measure	E_measure	MAE
DUTS	0.91725	0.89585	0.95016	0.02462
ECSSD	0.94301	0.94535	0.96665	0.02246
DUT	0.86163	0.80710	0.89227	0.04819
HKU-IS	0.93410	0.93195	0.96689	0.02154
PASCAL	0.88257	0.88097	0.92259	0.05063
SOD	0.86088	0.86524	0.89719	0.06080

The saliency maps of NJU2K,STERE,DES,NLPR,LFSD,SIP dataset in fully supervised manner are as: saliency maps2

	S_measure	F_measure	E_measure	MAE
NJU2K	0.92714	0.92206	0.95570	0.02777
STERE	0.91871	0.90235	0.95054	0.03114
DES	0.93886	0.93149	0.97045	0.01541
NLPR	0.93473	0.91704	0.96420	0.01941
LFSD	0.87720	0.86676	0.90660	0.05896
SIP	0.90487	0.91094	0.94262	0.03602

The saliency maps of DUTS, ECSSD, DUT, HKU-IS, PASCAL-S, SOD dataset in weakly supervised manner are as: saliency maps3

	S_measure	F_measure	E_measure	MAE
DUTS	0.860	0.823	0.915	0.040
ECSSD	0.906	0.913	0.951	0.038
DUT	0.838	0.768	0.888	0.056
HKU-IS	0.899	0.893	0.954	0.034
PASCAL	0.848	0.823	0.902	0.065
SOD	0.817	0.818	0.872	0.080

The camouflage maps of CAMO, CHAMELEON, COD10K, NC4K dataset in weakly supervised manner are as: camouflage maps

	S_measure	F_measure	E_measure	MAE
CAMO	0.86132	0.84558	0.92487	0.04681
CHAMELEON	0.89951	0.86423	0.95401	0.02289
COD10K	0.85013	0.78902	0.92432	0.02558
NC4K	0.87993	0.85659	0.93243	0.03346

Please drop me an email for further problems or discussion: maoyuxin@mail.nwpu.edu.cn