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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Seungwook Kim",
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  "author": "Seungwook Kim",
            
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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.2311.18540",
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    "https://doi.org/10.48550/arXiv.2311.18540",
            
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    "https://doi.org/10.48550/arXiv.2007.10587"
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    "https://doi.org/10.48550/arXiv.2007.10587"
            
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  ],
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  "defined_in": "https://doi.org/10.48550/arXiv.2311.04336",
            
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  "doi": "10.57702/8lyxlima",
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  "doi": "10.57702/8lyxlima",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-03",
            
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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": "Juhong Min",
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      "extra_author": "Juhong Min",
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Minsu Cho",
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      "extra_author": "Minsu Cho",
            
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      "orcid": ""
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      "name": "semantic-matching",
            
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      "title": "Semantic Matching"
            
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      "title": "Visual Correspondence"
            
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  "landing_page": "https://cvlab.postech.ac.kr/research/HCCNet",
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  "name": "pf-willow",
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  "name": "pf-willow",
            
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  "notes": "The proposed method, dubbed Dynamic Hyperpixel Flow, 
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  "notes": "The proposed method, dubbed Dynamic Hyperpixel Flow, 
            
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learns to compose hypercolumn features on the fly by selecting a small 
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learns to compose hypercolumn features on the fly by selecting a small 
            
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number of relevant layers from a deep convolutional neural network.",
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number of relevant layers from a deep convolutional neural network.",
            
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}
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}