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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Jianshu Li",
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  "author": "Jianshu Li",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.48550/arXiv.1904.04536"
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    "https://doi.org/10.48550/arXiv.1904.04536"
            
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  ],
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  ],
            
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  "defined_in": "https://doi.org/10.48550/arXiv.1705.07206",
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  "defined_in": "https://doi.org/10.48550/arXiv.1705.07206",
            
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  "doi": "10.57702/tj3t36k5",
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  "doi": "10.57702/tj3t36k5",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-16",
            
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  "domain": "https://service.tib.eu/ldmservice",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Yidong Li",
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      "extra_author": "Yidong Li",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Jian Zhao",
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      "extra_author": "Jian Zhao",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Yunchao Wei",
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      "extra_author": "Yunchao Wei",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Congyan Lang",
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      "extra_author": "Congyan Lang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Jiashi Feng",
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      "extra_author": "Jiashi Feng",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Shuicheng Yan",
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      "extra_author": "Shuicheng Yan",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Terence Sim",
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      "extra_author": "Terence Sim",
            
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      "orcid": ""
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      "orcid": ""
            
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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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      "display_name": "Human Parsing",
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      "display_name": "Human Parsing",
            
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      "name": "human-parsing",
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      "name": "human-parsing",
            
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      "title": "Human Parsing"
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      "title": "Human Parsing"
            
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    }
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  "landing_page": "https://ai.stanford.edu/~jgao/papers/ATR.pdf",
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  "landing_page": "https://ai.stanford.edu/~jgao/papers/ATR.pdf",
            
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  "license_title": null,
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  "metadata_modified": "2024-12-16T20:17:54.069382",
            
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  "name": "atr-dataset",
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  "name": "atr-dataset",
            
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  "notes": "Human parsing has recently attracted a huge amount of 
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  "notes": "Human parsing has recently attracted a huge amount of 
            
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interests and achieved great progress with the advance of deep 
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interests and achieved great progress with the advance of deep 
            
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convolutional neural networks and large-scale datasets. Most of the 
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convolutional neural networks and large-scale datasets. Most of the 
            
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prior works focus on developing new structures and auxiliary 
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prior works focus on developing new structures and auxiliary 
            
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information guidance to improve general feature representation, such 
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information guidance to improve general feature representation, such 
            
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as dilated convolution, LSTM structure, encoder-decoder architecture, 
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as dilated convolution, LSTM structure, encoder-decoder architecture, 
            
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and human pose constraints. Although these methods show promising 
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and human pose constraints. Although these methods show promising 
            
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results on each human parsing dataset, they directly use one flat 
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results on each human parsing dataset, they directly use one flat 
            
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prediction layer to classify all labels, which disregards the 
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prediction layer to classify all labels, which disregards the 
            
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intrinsic semantic correlations across concepts and utilize the 
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intrinsic semantic correlations across concepts and utilize the 
            
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annotations in an inefficient way.",
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annotations in an inefficient way.",
            
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  "title": "ATR dataset",
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}
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}