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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Yiming Zhang",
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  "author": "Yiming Zhang",
            
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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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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.2404.09387",
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  "defined_in": "https://doi.org/10.48550/arXiv.2404.09387",
            
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  "doi": "10.57702/tk8iqkz1",
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  "doi": "10.57702/tk8iqkz1",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-03",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "domain": "https://service.tib.eu/ldmservice",
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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": "Zhuokai Zhao",
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      "extra_author": "Zhuokai 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": "Zhaorun Chen",
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      "extra_author": "Zhaorun Chen",
            
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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": "Zhili Feng",
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      "extra_author": "Zhili 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": "Zenghui Ding",
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      "extra_author": "Zenghui Ding",
            
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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": "Yining Sun",
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      "extra_author": "Yining Sun",
            
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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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    {
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      "description": "",
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      "description": "",
            
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      "display_name": "Multimodal Learning",
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      "display_name": "Multimodal Learning",
            
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      "name": "multimodal-learning",
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      "name": "multimodal-learning",
            
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      "title": "Multimodal Learning"
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      "title": "Multimodal Learning"
            
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    },
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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": "Vision-Language Models",
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      "display_name": "Vision-Language Models",
            
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      "image_display_url": "",
            
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      "name": "vision-language-models",
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      "name": "vision-language-models",
            
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      "title": "Vision-Language Models"
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      "title": "Vision-Language Models"
            
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    }
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    }
            
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  ],
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  ],
            
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  "id": "0464b3d2-b4f7-459c-bc7e-98991a9ec7b3",
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  "isopen": false,
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  "isopen": false,
            
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  "landing_page": "https://github.com/Jam1ezhang/RankCLIP",
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  "landing_page": "https://github.com/Jam1ezhang/RankCLIP",
            
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  "license_title": null,
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  "metadata_created": "2024-12-03T10:16:14.695827",
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  "metadata_modified": "2024-12-03T10:16:15.066142",
            
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  "name": "rankclip--ranking-consistent-language-image-pretraining",
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  "name": "rankclip--ranking-consistent-language-image-pretraining",
            
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  "notes": "Self-supervised contrastive learning models, such as CLIP, 
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  "notes": "Self-supervised contrastive learning models, such as CLIP, 
            
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have set new benchmarks for vision-language models in many downstream 
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have set new benchmarks for vision-language models in many downstream 
            
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tasks. However, their dependency on rigid one-to-one mappings 
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tasks. However, their dependency on rigid one-to-one mappings 
            
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overlooks the complex and often multifaceted relationships between and 
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overlooks the complex and often multifaceted relationships between and 
            
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within texts and images. To this end, we introduce RANKCLIP, a novel 
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within texts and images. To this end, we introduce RANKCLIP, a novel 
            
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pretraining method that extends beyond the rigid one-to-one matching 
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pretraining method that extends beyond the rigid one-to-one matching 
            
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framework of CLIP and its variants.",
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framework of CLIP and its variants.",
            
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distributions based on DCAT.",
            
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  "tags": [
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  "tags": [
            
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    {
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    {
            
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      "display_name": "contrastive learning",
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      "display_name": "contrastive learning",
            
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      "id": "74000786-0a7d-485e-a5e9-edee3834ea39",
            
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      "name": "contrastive learning",
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      "name": "contrastive learning",
            
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      "state": "active",
            
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      "display_name": "multimodal learning",
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      "display_name": "multimodal learning",
            
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      "id": "0decb357-2292-4127-9886-35e2f72c9df2",
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      "id": "0decb357-2292-4127-9886-35e2f72c9df2",
            
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      "name": "multimodal learning",
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      "name": "multimodal learning",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
            
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    },
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    {
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    {
            
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      "display_name": "vision-language models",
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      "display_name": "vision-language models",
            
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      "id": "f02eea8d-0e99-468f-884c-6fcdf16465ce",
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      "id": "f02eea8d-0e99-468f-884c-6fcdf16465ce",
            
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      "name": "vision-language models",
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      "name": "vision-language models",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "RANKCLIP: Ranking-Consistent Language-Image Pretraining",
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  "title": "RANKCLIP: Ranking-Consistent Language-Image Pretraining",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}