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  "author": "Bruno Sauvalle",
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  "author": "Bruno Sauvalle",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [
            
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    "https://doi.org/10.48550/arXiv.2205.13271"
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    {
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    {
            
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      "extra_author": "Arnaud de La Fortelle",
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      "extra_author": "Arnaud de La Fortelle",
            
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      "name": "object-segmentation",
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      "name": "object-segmentation",
            
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      "title": "Object Segmentation"
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      "title": "Object Segmentation"
            
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    },
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "name": "unsupervised-multi-object-segmentation",
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      "name": "unsupervised-multi-object-segmentation",
            
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      "title": "Unsupervised Multi-Object Segmentation"
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      "title": "Unsupervised Multi-Object Segmentation"
            
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  "name": "clevrtex",
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  "name": "clevrtex",
            
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  "notes": "CLEVRTEX is a synthetic dataset designed to present the 
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  "notes": "CLEVRTEX is a synthetic dataset designed to present the 
            
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next challenge in unsupervised multi-object learning. It introduces 
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next challenge in unsupervised multi-object learning. It introduces 
            
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confounding visual aspects such as texture, irregular shapes, and 
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confounding visual aspects such as texture, irregular shapes, and 
            
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various materials while maintaining control over scene composition.",
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various materials while maintaining control over scene composition.",
            
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      "name": "unsupervised multi-object segmentation",
            
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}