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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Jiahao Lu",
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  "author": "Jiahao Lu",
            
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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.2107.14230",
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    "https://doi.org/10.48550/arXiv.2107.14230",
            
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    "https://doi.org/10.1109/ICME52920.2022.9859996",
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    "https://doi.org/10.1109/ICME52920.2022.9859996",
            
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    "https://doi.org/10.48550/arXiv.1909.13603",
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    "https://doi.org/10.48550/arXiv.1909.13603",
            
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    "https://doi.org/10.48550/arXiv.2211.09375"
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    "https://doi.org/10.48550/arXiv.2211.09375"
            
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  ],
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  "defined_in": "https://doi.org/10.48550/arXiv.2004.01170",
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  "defined_in": "https://doi.org/10.48550/arXiv.2004.01170",
            
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  "doi": "10.57702/z9chl0jz",
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  "doi": "10.57702/z9chl0jz",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-02",
            
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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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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Jiacheng Deng",
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      "extra_author": "Jiacheng Deng",
            
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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": "Tianzhu Zhang",
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      "extra_author": "Tianzhu Zhang",
            
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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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      "description": "",
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      "description": "",
            
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      "display_name": "3D Object Detection",
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      "name": "3d-object-detection",
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      "name": "3d-object-detection",
            
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      "title": "3D Object Detection"
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      "title": "3D Object Detection"
            
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      "name": "3d-object-segmentation",
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      "name": "3d-object-segmentation",
            
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      "title": "3D Object Segmentation"
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      "title": "3D Object Segmentation"
            
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      "name": "3d-reconstruction",
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      "name": "3d-reconstruction",
            
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      "title": "3D Reconstruction"
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      "title": "3D Reconstruction"
            
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    },
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      "description": "",
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      "name": "3d-scene-understanding",
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      "name": "3d-scene-understanding",
            
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      "title": "3D Scene Understanding"
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      "title": "3D Scene Understanding"
            
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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": "3D Segmentation",
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      "display_name": "3D Segmentation",
            
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      "image_display_url": "",
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      "image_display_url": "",
            
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      "name": "3d-segmentation",
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      "name": "3d-segmentation",
            
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      "title": "3D Segmentation"
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      "title": "3D Segmentation"
            
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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": "Indoor Scenes",
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      "display_name": "Indoor Scenes",
            
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      "id": "1cdde29d-6358-4f23-823c-0e7caad9ae2e",
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      "id": "1cdde29d-6358-4f23-823c-0e7caad9ae2e",
            
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      "image_display_url": "",
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      "image_display_url": "",
            
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      "name": "indoor-scenes",
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      "name": "indoor-scenes",
            
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      "title": "Indoor Scenes"
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      "title": "Indoor Scenes"
            
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    }
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    }
            
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  ],
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  "isopen": false,
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  "isopen": false,
            
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  "landing_page": "https://www.scannet.org/",
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  "landing_page": "https://www.scannet.org/",
            
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  "license_title": null,
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  "license_title": null,
            
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  "link_orkg": "",
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  "link_orkg": "",
            
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  "metadata_created": "2024-12-02T23:55:40.668404",
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  "metadata_created": "2024-12-02T23:55:40.668404",
            
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  "metadata_modified": "2024-12-02T23:55:41.223982",
            
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  "name": "scannetv2",
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  "name": "scannetv2",
            
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  "notes": "The dataset used in the paper is ScanNetV2, a real-world 
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  "notes": "The dataset used in the paper is ScanNetV2, a real-world 
            
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dataset for indoor scenes. It contains 1205 training scenes and 312 
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dataset for indoor scenes. It contains 1205 training scenes and 312 
            
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testing scenes, with instance-level object bounding box labels 
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testing scenes, with instance-level object bounding box labels 
            
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provided for 18 categories.",
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provided for 18 categories.",
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    },
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    {
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    {
            
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      "display_name": "3D Scene Understanding",
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      "display_name": "3D Scene Understanding",
            
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      "name": "3D Scene Understanding",
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      "name": "3D Scene Understanding",
            
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    },
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    },
            
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      "display_name": "Indoor Scenes",
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    },
            
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      "name": "Instance Segmentation",
            
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    {
            
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      "display_name": "Point Clouds",
            
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      "id": "cd5e5c68-6deb-4e83-b810-0756f3961446",
            
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      "name": "Point Clouds",
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    },
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    },
            
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    {
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    {
            
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      "display_name": "ScanNet",
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      "display_name": "ScanNet",
            
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      "id": "7d644041-616d-44ba-8c6f-2679ae208bb4",
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      "id": "7d644041-616d-44ba-8c6f-2679ae208bb4",
            
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      "name": "ScanNet",
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      "name": "ScanNet",
            
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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": "Segmentation",
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      "display_name": "Segmentation",
            
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      "id": "afba543e-f91f-4800-834e-77535c9e8dac",
            
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      "name": "Segmentation",
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      "state": "active",
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "Semantic Segmentation",
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      "display_name": "Semantic Segmentation",
            
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      "id": "809ad6af-28cd-43bd-974d-055a5c0f2973",
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      "id": "809ad6af-28cd-43bd-974d-055a5c0f2973",
            
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      "name": "Semantic Segmentation",
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      "name": "Semantic Segmentation",
            
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      "state": "active",
234
      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
195
    },
236
    },
            
196
    {
237
    {
            
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      "display_name": "indoor scenes",
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      "display_name": "indoor scenes",
            
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      "id": "7c39b0f6-b707-459a-9d7e-7e2625177b9f",
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      "id": "7c39b0f6-b707-459a-9d7e-7e2625177b9f",
            
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      "name": "indoor scenes",
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      "name": "indoor scenes",
            
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      "state": "active",
241
      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
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    },
            
203
    {
244
    {
            
204
      "display_name": "object detection",
245
      "display_name": "object detection",
            
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      "id": "607283c7-9e12-4167-9101-7f8078fb6537",
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      "id": "607283c7-9e12-4167-9101-7f8078fb6537",
            
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      "name": "object detection",
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      "name": "object detection",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
209
    },
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    },
            
210
    {
251
    {
            
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      "display_name": "object segmentation",
252
      "display_name": "object segmentation",
            
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      "id": "6a47ecb7-e3fb-43a9-b0a8-1434387d3ee4",
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      "id": "6a47ecb7-e3fb-43a9-b0a8-1434387d3ee4",
            
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      "name": "object segmentation",
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      "name": "object segmentation",
            
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      "state": "active",
255
      "state": "active",
            
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      "vocabulary_id": null
256
      "vocabulary_id": null
            
216
    }
257
    }
            
217
  ],
258
  ],
            
218
  "title": "ScanNetV2",
259
  "title": "ScanNetV2",
            
219
  "type": "dataset",
260
  "type": "dataset",
            
220
  "version": ""
261
  "version": ""
            
221
}
262
}