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Unsupervised Object Segmentation On Clevrtex

Metrics

MSE
mIoU

Results

Performance results of various models on this benchmark

Model Name
MSE
mIoU
Paper TitleRepository
IODINE340± 329.16± 0.75ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
MN335± 1 10.46± 0.10ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
eMORL347± 20 30.17± 2.60ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
SA254± 822.58± 2.07ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
MONet146± 719.78± 1.02ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
GenV2315±106 7.93± 1.53 ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
SPAIR1101± 2 0.0 ± 0.0ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
GNM383± 2 42.25± 0.18ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
SPACE298± 80 9.14± 3.46 ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
DTI438± 2233.79± 1.30ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation-
AST167± 166.62± 0.80Unsupervised Multi-object Segmentation Using Attention and Soft-argmax-
AST-Seg-B3-CT139±779.58±0.54Unsupervised Multi-object Segmentation Using Attention and Soft-argmax-
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Unsupervised Object Segmentation On Clevrtex | SOTA | HyperAI