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Unsupervised Object Segmentation On Clevrtex

Métriques

MSE

mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	MSE	mIoU	Paper Title	Repository
IODINE	340± 3	29.16± 0.75	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
MN	335± 1	10.46± 0.10	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
eMORL	347± 20	30.17± 2.60	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
SA	254± 8	22.58± 2.07	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
MONet	146± 7	19.78± 1.02	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
GenV2	315±106	7.93± 1.53	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
SPAIR	1101± 2	0.0 ± 0.0	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
GNM	383± 2	42.25± 0.18	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
SPACE	298± 80	9.14± 3.46	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
DTI	438± 22	33.79± 1.30	ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
AST	167± 1	66.62± 0.80	Unsupervised Multi-object Segmentation Using Attention and Soft-argmax
AST-Seg-B3-CT	139±7	79.58±0.54	Unsupervised Multi-object Segmentation Using Attention and Soft-argmax

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