Table 1 Comparison of performance...

Table 1

Comparison of performance metrics between proposed and benchmark methods on the COD10K dataset. Only models with less than 50G Multiply-Accumulate Operations (MACs) were considered. The top-performing method for each metric on each dataset is highlighted in bold, while the second-best method is underscored.

Model	Pub/Year	Input	s_a ↑	$F_{β}^{w} ↑$	M ↓	$E_{ϕ}^{m n} ↑$	Para.	MACs
SINet [10]	CVPR’20	352²	0.776	0.631	0.043	0.864	48.95M	19.42G
C2FNet [32]	IJCAF21	352²	0.813	0.686	0.036	0.890	28.41M	13.12G
TINet [48]	AAAF21	352²	0.793	0.635	0.042	0.861	28.56M	8.58G
JSCOD [21]	CVPR’21	352²	0.809	0.684	0.035	0.884	121.63M	25.20G
LSR [24]	CVPR’21	352²	0.804	0.673	0.037	0.880	57.90M	25.21G
PFNet [28]	CVPR’21	416²	0.800	0.660	0.040	0.877	45.64M	26.54G
C2FNet-V2 [1]	TCSVT’22	352²	0.811	0.691	0.036	0.887	44.94M	18.10G
ERRNet [16]	PR’22	352²	0.786	0.630	0.043	0.867	69.76M	20.05G
TPRNet [44]	TVC J’22	352²	0.817	0.683	0.036	0.887	32.95M	12.98G
FAPNet [46]	TIP’22	352²	0.822	0.694	0.036	0.888	29.52M	29.69G
BSANet [47]	AAAI’22	384²	0.818	0.699	0.034	0.891	32.58M	29.70G
SegMaR [17]	CVPR’22	352²	0.833	0.724	0.034	0.899	56.21M	33.63G
SINetV2 [8]	TPAMI’22	352²	0.815	0.680	0.037	0.887	26.98M	12.28G
CRNet [13]	AAAI’23	320²	0.733	0.576	0.049	0.832	32.65M	11.83G
DGNet-S [15]	MIR’23	352²	0.810	0.672	0.036	0.888	7.02M	2.77G
DGNet [15]	MIR’23	352²	0.822	0.693	0.033	0.896	19.22M	1.20G
GreenCOD-D3-1000	-	672²	0.797	0.701	0.033	0.881	16.83M	13.70G
GreenCOD-D3-10000	-	672²	0.807	0.715	0.032	0.893	17.62M	15.06G
GreenCOD-D6-1000	-	672²	0.804	0.709	0.032	0.891	17.50M	13.78G
GreenCOD-D6-10000	-	672²	0.813	0.724	0.031	0.895	24.34M	16.22G

Model	Pub/Year	Input	s_a ↑	$F_{β}^{w} ↑$	M ↓	$E_{ϕ}^{m n} ↑$	Para.	MACs
SINet [10]	CVPR’20	352²	0.776	0.631	0.043	0.864	48.95M	19.42G
C2FNet [32]	IJCAF21	352²	0.813	0.686	0.036	0.890	28.41M	13.12G
TINet [48]	AAAF21	352²	0.793	0.635	0.042	0.861	28.56M	8.58G
JSCOD [21]	CVPR’21	352²	0.809	0.684	0.035	0.884	121.63M	25.20G
LSR [24]	CVPR’21	352²	0.804	0.673	0.037	0.880	57.90M	25.21G
PFNet [28]	CVPR’21	416²	0.800	0.660	0.040	0.877	45.64M	26.54G
C2FNet-V2 [1]	TCSVT’22	352²	0.811	0.691	0.036	0.887	44.94M	18.10G
ERRNet [16]	PR’22	352²	0.786	0.630	0.043	0.867	69.76M	20.05G
TPRNet [44]	TVC J’22	352²	0.817	0.683	0.036	0.887	32.95M	12.98G
FAPNet [46]	TIP’22	352²	0.822	0.694	0.036	0.888	29.52M	29.69G
BSANet [47]	AAAI’22	384²	0.818	0.699	0.034	0.891	32.58M	29.70G
SegMaR [17]	CVPR’22	352²	0.833	0.724	0.034	0.899	56.21M	33.63G
SINetV2 [8]	TPAMI’22	352²	0.815	0.680	0.037	0.887	26.98M	12.28G
CRNet [13]	AAAI’23	320²	0.733	0.576	0.049	0.832	32.65M	11.83G
DGNet-S [15]	MIR’23	352²	0.810	0.672	0.036	0.888	7.02M	2.77G
DGNet [15]	MIR’23	352²	0.822	0.693	0.033	0.896	19.22M	1.20G
GreenCOD-D3-1000	-	672²	0.797	0.701	0.033	0.881	16.83M	13.70G
GreenCOD-D3-10000	-	672²	0.807	0.715	0.032	0.893	17.62M	15.06G
GreenCOD-D6-1000	-	672²	0.804	0.709	0.032	0.891	17.50M	13.78G
GreenCOD-D6-10000	-	672²	0.813	0.724	0.031	0.895	24.34M	16.22G

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