Changes
On December 2, 2024 at 9:38:51 PM UTC,
admin:
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Changed value of field
doi_status
toTrue
in NYUv2 -
Changed value of field
doi_date_published
to2024-12-02
in NYUv2 -
Added resource Original Metadata to NYUv2
| f | 1 | { | f | 1 | { |
| 2 | "access_rights": "", | 2 | "access_rights": "", | ||
| 3 | "author": "Jiaming Liu", | 3 | "author": "Jiaming Liu", | ||
| 4 | "author_email": "", | 4 | "author_email": "", | ||
| 5 | "citation": [ | 5 | "citation": [ | ||
| 6 | "https://doi.org/10.48550/arXiv.1901.10034", | 6 | "https://doi.org/10.48550/arXiv.1901.10034", | ||
| 7 | "https://doi.org/10.48550/arXiv.2307.15429", | 7 | "https://doi.org/10.48550/arXiv.2307.15429", | ||
| 8 | "https://doi.org/10.48550/arXiv.2007.13727", | 8 | "https://doi.org/10.48550/arXiv.2007.13727", | ||
| 9 | "https://doi.org/10.48550/arXiv.2306.05242", | 9 | "https://doi.org/10.48550/arXiv.2306.05242", | ||
| 10 | "https://doi.org/10.48550/arXiv.2009.09976", | 10 | "https://doi.org/10.48550/arXiv.2009.09976", | ||
| 11 | "https://doi.org/10.48550/arXiv.2211.14037", | 11 | "https://doi.org/10.48550/arXiv.2211.14037", | ||
| 12 | "https://doi.org/10.48550/arXiv.2002.12114" | 12 | "https://doi.org/10.48550/arXiv.2002.12114" | ||
| 13 | ], | 13 | ], | ||
| 14 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 14 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
| 15 | "defined_in": "https://doi.org/10.48550/arXiv.2007.05441", | 15 | "defined_in": "https://doi.org/10.48550/arXiv.2007.05441", | ||
| 16 | "doi": "10.57702/7osc5sda", | 16 | "doi": "10.57702/7osc5sda", | ||
| n | 17 | "doi_date_published": null, | n | 17 | "doi_date_published": "2024-12-02", |
| 18 | "doi_publisher": "TIB", | 18 | "doi_publisher": "TIB", | ||
| n | 19 | "doi_status": false, | n | 19 | "doi_status": true, |
| 20 | "domain": "https://service.tib.eu/ldmservice", | 20 | "domain": "https://service.tib.eu/ldmservice", | ||
| 21 | "extra_authors": [ | 21 | "extra_authors": [ | ||
| 22 | { | 22 | { | ||
| 23 | "extra_author": "Qizhe Zhang", | 23 | "extra_author": "Qizhe Zhang", | ||
| 24 | "orcid": "" | 24 | "orcid": "" | ||
| 25 | }, | 25 | }, | ||
| 26 | { | 26 | { | ||
| 27 | "extra_author": "Jianing Li", | 27 | "extra_author": "Jianing Li", | ||
| 28 | "orcid": "" | 28 | "orcid": "" | ||
| 29 | }, | 29 | }, | ||
| 30 | { | 30 | { | ||
| 31 | "extra_author": "Ming Lu", | 31 | "extra_author": "Ming Lu", | ||
| 32 | "orcid": "" | 32 | "orcid": "" | ||
| 33 | }, | 33 | }, | ||
| 34 | { | 34 | { | ||
| 35 | "extra_author": "Tiejun Huang", | 35 | "extra_author": "Tiejun Huang", | ||
| 36 | "orcid": "" | 36 | "orcid": "" | ||
| 37 | }, | 37 | }, | ||
| 38 | { | 38 | { | ||
| 39 | "extra_author": "Shanghang Zhang", | 39 | "extra_author": "Shanghang Zhang", | ||
| 40 | "orcid": "" | 40 | "orcid": "" | ||
| 41 | } | 41 | } | ||
| 42 | ], | 42 | ], | ||
| 43 | "groups": [ | 43 | "groups": [ | ||
| 44 | { | 44 | { | ||
| 45 | "description": "", | 45 | "description": "", | ||
| 46 | "display_name": "3D Reconstruction", | 46 | "display_name": "3D Reconstruction", | ||
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| 48 | "image_display_url": "", | 48 | "image_display_url": "", | ||
| 49 | "name": "3d-reconstruction", | 49 | "name": "3d-reconstruction", | ||
| 50 | "title": "3D Reconstruction" | 50 | "title": "3D Reconstruction" | ||
| 51 | }, | 51 | }, | ||
| 52 | { | 52 | { | ||
| 53 | "description": "", | 53 | "description": "", | ||
| 54 | "display_name": "Computer Vision", | 54 | "display_name": "Computer Vision", | ||
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| 56 | "image_display_url": "", | 56 | "image_display_url": "", | ||
| 57 | "name": "computer-vision", | 57 | "name": "computer-vision", | ||
| 58 | "title": "Computer Vision" | 58 | "title": "Computer Vision" | ||
| 59 | }, | 59 | }, | ||
| 60 | { | 60 | { | ||
| 61 | "description": "", | 61 | "description": "", | ||
| 62 | "display_name": "Depth Estimation", | 62 | "display_name": "Depth Estimation", | ||
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| 64 | "image_display_url": "", | 64 | "image_display_url": "", | ||
| 65 | "name": "depth-estimation", | 65 | "name": "depth-estimation", | ||
| 66 | "title": "Depth Estimation" | 66 | "title": "Depth Estimation" | ||
| 67 | }, | 67 | }, | ||
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| 69 | "description": "", | 69 | "description": "", | ||
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| 72 | "image_display_url": "", | 72 | "image_display_url": "", | ||
| 73 | "name": "image-segmentation", | 73 | "name": "image-segmentation", | ||
| 74 | "title": "Image Segmentation" | 74 | "title": "Image Segmentation" | ||
| 75 | }, | 75 | }, | ||
| 76 | { | 76 | { | ||
| 77 | "description": "", | 77 | "description": "", | ||
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| 80 | "image_display_url": "", | 80 | "image_display_url": "", | ||
| 81 | "name": "indoor-scene-understanding", | 81 | "name": "indoor-scene-understanding", | ||
| 82 | "title": "Indoor Scene Understanding" | 82 | "title": "Indoor Scene Understanding" | ||
| 83 | }, | 83 | }, | ||
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| 86 | "display_name": "Indoor Segmentation", | 86 | "display_name": "Indoor Segmentation", | ||
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| 88 | "image_display_url": "", | 88 | "image_display_url": "", | ||
| 89 | "name": "indoor-segmentation", | 89 | "name": "indoor-segmentation", | ||
| 90 | "title": "Indoor Segmentation" | 90 | "title": "Indoor Segmentation" | ||
| 91 | }, | 91 | }, | ||
| 92 | { | 92 | { | ||
| 93 | "description": "", | 93 | "description": "", | ||
| 94 | "display_name": "Object Detection", | 94 | "display_name": "Object Detection", | ||
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| 96 | "image_display_url": "", | 96 | "image_display_url": "", | ||
| 97 | "name": "object-detection", | 97 | "name": "object-detection", | ||
| 98 | "title": "Object Detection" | 98 | "title": "Object Detection" | ||
| 99 | }, | 99 | }, | ||
| 100 | { | 100 | { | ||
| 101 | "description": "", | 101 | "description": "", | ||
| 102 | "display_name": "Scene Analysis", | 102 | "display_name": "Scene Analysis", | ||
| 103 | "id": "09b016b4-749a-4dc8-9af5-ef7f0035a0ef", | 103 | "id": "09b016b4-749a-4dc8-9af5-ef7f0035a0ef", | ||
| 104 | "image_display_url": "", | 104 | "image_display_url": "", | ||
| 105 | "name": "scene-analysis", | 105 | "name": "scene-analysis", | ||
| 106 | "title": "Scene Analysis" | 106 | "title": "Scene Analysis" | ||
| 107 | }, | 107 | }, | ||
| 108 | { | 108 | { | ||
| 109 | "description": "", | 109 | "description": "", | ||
| 110 | "display_name": "Semantic Segmentation", | 110 | "display_name": "Semantic Segmentation", | ||
| 111 | "id": "8c3f2eee-f5f9-464d-9c0a-1a5e7a925c0e", | 111 | "id": "8c3f2eee-f5f9-464d-9c0a-1a5e7a925c0e", | ||
| 112 | "image_display_url": "", | 112 | "image_display_url": "", | ||
| 113 | "name": "semantic-segmentation", | 113 | "name": "semantic-segmentation", | ||
| 114 | "title": "Semantic Segmentation" | 114 | "title": "Semantic Segmentation" | ||
| 115 | }, | 115 | }, | ||
| 116 | { | 116 | { | ||
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| 118 | "display_name": "Surface Normal Prediction", | 118 | "display_name": "Surface Normal Prediction", | ||
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| 120 | "image_display_url": "", | 120 | "image_display_url": "", | ||
| 121 | "name": "surface-normal-prediction", | 121 | "name": "surface-normal-prediction", | ||
| 122 | "title": "Surface Normal Prediction" | 122 | "title": "Surface Normal Prediction" | ||
| 123 | } | 123 | } | ||
| 124 | ], | 124 | ], | ||
| 125 | "id": "7350c7a9-3e98-4327-b03b-69be0ae1c034", | 125 | "id": "7350c7a9-3e98-4327-b03b-69be0ae1c034", | ||
| 126 | "isopen": false, | 126 | "isopen": false, | ||
| 127 | "landing_page": | 127 | "landing_page": | ||
| 128 | "https://www.cs.nyu.edu/~ylclab/research/datasets/nyu-depth-v2.html", | 128 | "https://www.cs.nyu.edu/~ylclab/research/datasets/nyu-depth-v2.html", | ||
| 129 | "license_title": null, | 129 | "license_title": null, | ||
| 130 | "link_orkg": "", | 130 | "link_orkg": "", | ||
| 131 | "metadata_created": "2024-12-02T21:38:50.015927", | 131 | "metadata_created": "2024-12-02T21:38:50.015927", | ||
| n | 132 | "metadata_modified": "2024-12-02T21:38:50.015933", | n | 132 | "metadata_modified": "2024-12-02T21:38:50.721442", |
| 133 | "name": "nyuv2", | 133 | "name": "nyuv2", | ||
| 134 | "notes": "Multi-task learning (MTL) research is broadly divided into | 134 | "notes": "Multi-task learning (MTL) research is broadly divided into | ||
| 135 | two categories: one is to learn the correlation between tasks through | 135 | two categories: one is to learn the correlation between tasks through | ||
| 136 | model structures, and the other is to balance the joint training | 136 | model structures, and the other is to balance the joint training | ||
| 137 | process of all tasks through optimization algorithms.", | 137 | process of all tasks through optimization algorithms.", | ||
| n | 138 | "num_resources": 0, | n | 138 | "num_resources": 1, |
| 139 | "num_tags": 19, | 139 | "num_tags": 19, | ||
| 140 | "organization": { | 140 | "organization": { | ||
| 141 | "approval_status": "approved", | 141 | "approval_status": "approved", | ||
| 142 | "created": "2024-11-25T12:11:38.292601", | 142 | "created": "2024-11-25T12:11:38.292601", | ||
| 143 | "description": "", | 143 | "description": "", | ||
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| 145 | "image_url": "", | 145 | "image_url": "", | ||
| 146 | "is_organization": true, | 146 | "is_organization": true, | ||
| 147 | "name": "no-organization", | 147 | "name": "no-organization", | ||
| 148 | "state": "active", | 148 | "state": "active", | ||
| 149 | "title": "No Organization", | 149 | "title": "No Organization", | ||
| 150 | "type": "organization" | 150 | "type": "organization" | ||
| 151 | }, | 151 | }, | ||
| 152 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 152 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
| 153 | "private": false, | 153 | "private": false, | ||
| 154 | "relationships_as_object": [], | 154 | "relationships_as_object": [], | ||
| 155 | "relationships_as_subject": [], | 155 | "relationships_as_subject": [], | ||
| t | 156 | "resources": [], | t | 156 | "resources": [ |
| 157 | { | ||||
| 158 | "cache_last_updated": null, | ||||
| 159 | "cache_url": null, | ||||
| 160 | "created": "2024-12-02T22:29:38", | ||||
| 161 | "data": [ | ||||
| 162 | "dcterms:title", | ||||
| 163 | "dcterms:accessRights", | ||||
| 164 | "dcterms:creator", | ||||
| 165 | "dcterms:description", | ||||
| 166 | "dcterms:issued", | ||||
| 167 | "dcterms:language", | ||||
| 168 | "dcterms:identifier", | ||||
| 169 | "dcat:theme", | ||||
| 170 | "dcterms:type", | ||||
| 171 | "dcat:keyword", | ||||
| 172 | "dcat:landingPage", | ||||
| 173 | "dcterms:hasVersion", | ||||
| 174 | "dcterms:format", | ||||
| 175 | "mls:task", | ||||
| 176 | "datacite:isDescribedBy" | ||||
| 177 | ], | ||||
| 178 | "description": "The json representation of the dataset with its | ||||
| 179 | distributions based on DCAT.", | ||||
| 180 | "format": "JSON", | ||||
| 181 | "hash": "", | ||||
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| 185 | "mimetype": "application/json", | ||||
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| 187 | "name": "Original Metadata", | ||||
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| 189 | "position": 0, | ||||
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| 191 | "size": 1850, | ||||
| 192 | "state": "active", | ||||
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| 194 | resource/22ac85f8-f48a-45b5-9f14-19dbf5090686/download/metadata.json", | ||||
| 195 | "url_type": "upload" | ||||
| 196 | } | ||||
| 197 | ], | ||||
| 157 | "services_used_list": "", | 198 | "services_used_list": "", | ||
| 158 | "state": "active", | 199 | "state": "active", | ||
| 159 | "tags": [ | 200 | "tags": [ | ||
| 160 | { | 201 | { | ||
| 161 | "display_name": "3D scene reconstruction", | 202 | "display_name": "3D scene reconstruction", | ||
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| 163 | "name": "3D scene reconstruction", | 204 | "name": "3D scene reconstruction", | ||
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| 165 | "vocabulary_id": null | 206 | "vocabulary_id": null | ||
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| 172 | "vocabulary_id": null | 213 | "vocabulary_id": null | ||
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| 177 | "name": "Depth Images", | 218 | "name": "Depth Images", | ||
| 178 | "state": "active", | 219 | "state": "active", | ||
| 179 | "vocabulary_id": null | 220 | "vocabulary_id": null | ||
| 180 | }, | 221 | }, | ||
| 181 | { | 222 | { | ||
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| 185 | "state": "active", | 226 | "state": "active", | ||
| 186 | "vocabulary_id": null | 227 | "vocabulary_id": null | ||
| 187 | }, | 228 | }, | ||
| 188 | { | 229 | { | ||
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| 191 | "name": "Indoor Scenes", | 232 | "name": "Indoor Scenes", | ||
| 192 | "state": "active", | 233 | "state": "active", | ||
| 193 | "vocabulary_id": null | 234 | "vocabulary_id": null | ||
| 194 | }, | 235 | }, | ||
| 195 | { | 236 | { | ||
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| 198 | "name": "Indoor Segmentation", | 239 | "name": "Indoor Segmentation", | ||
| 199 | "state": "active", | 240 | "state": "active", | ||
| 200 | "vocabulary_id": null | 241 | "vocabulary_id": null | ||
| 201 | }, | 242 | }, | ||
| 202 | { | 243 | { | ||
| 203 | "display_name": "Instance Segmentation", | 244 | "display_name": "Instance Segmentation", | ||
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| 205 | "name": "Instance Segmentation", | 246 | "name": "Instance Segmentation", | ||
| 206 | "state": "active", | 247 | "state": "active", | ||
| 207 | "vocabulary_id": null | 248 | "vocabulary_id": null | ||
| 208 | }, | 249 | }, | ||
| 209 | { | 250 | { | ||
| 210 | "display_name": "NYUv2", | 251 | "display_name": "NYUv2", | ||
| 211 | "id": "f38a103e-ef81-4fc7-a558-3de4df053d06", | 252 | "id": "f38a103e-ef81-4fc7-a558-3de4df053d06", | ||
| 212 | "name": "NYUv2", | 253 | "name": "NYUv2", | ||
| 213 | "state": "active", | 254 | "state": "active", | ||
| 214 | "vocabulary_id": null | 255 | "vocabulary_id": null | ||
| 215 | }, | 256 | }, | ||
| 216 | { | 257 | { | ||
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| 219 | "name": "RGB Images", | 260 | "name": "RGB Images", | ||
| 220 | "state": "active", | 261 | "state": "active", | ||
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| 223 | { | 264 | { | ||
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| 226 | "name": "RGB-D", | 267 | "name": "RGB-D", | ||
| 227 | "state": "active", | 268 | "state": "active", | ||
| 228 | "vocabulary_id": null | 269 | "vocabulary_id": null | ||
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| 230 | { | 271 | { | ||
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| 233 | "name": "RGB-D images", | 274 | "name": "RGB-D images", | ||
| 234 | "state": "active", | 275 | "state": "active", | ||
| 235 | "vocabulary_id": null | 276 | "vocabulary_id": null | ||
| 236 | }, | 277 | }, | ||
| 237 | { | 278 | { | ||
| 238 | "display_name": "Scene Classification", | 279 | "display_name": "Scene Classification", | ||
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| 244 | { | 285 | { | ||
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| 249 | "vocabulary_id": null | 290 | "vocabulary_id": null | ||
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| 251 | { | 292 | { | ||
| 252 | "display_name": "Surface Normal Prediction", | 293 | "display_name": "Surface Normal Prediction", | ||
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| 257 | }, | 298 | }, | ||
| 258 | { | 299 | { | ||
| 259 | "display_name": "depth estimation", | 300 | "display_name": "depth estimation", | ||
| 260 | "id": "3c08a798-cec3-4682-a668-4f95d6d8ad18", | 301 | "id": "3c08a798-cec3-4682-a668-4f95d6d8ad18", | ||
| 261 | "name": "depth estimation", | 302 | "name": "depth estimation", | ||
| 262 | "state": "active", | 303 | "state": "active", | ||
| 263 | "vocabulary_id": null | 304 | "vocabulary_id": null | ||
| 264 | }, | 305 | }, | ||
| 265 | { | 306 | { | ||
| 266 | "display_name": "image segmentation", | 307 | "display_name": "image segmentation", | ||
| 267 | "id": "7eaed78e-c73a-4929-a8c9-60265069f59a", | 308 | "id": "7eaed78e-c73a-4929-a8c9-60265069f59a", | ||
| 268 | "name": "image segmentation", | 309 | "name": "image segmentation", | ||
| 269 | "state": "active", | 310 | "state": "active", | ||
| 270 | "vocabulary_id": null | 311 | "vocabulary_id": null | ||
| 271 | }, | 312 | }, | ||
| 272 | { | 313 | { | ||
| 273 | "display_name": "indoor scenes", | 314 | "display_name": "indoor scenes", | ||
| 274 | "id": "7c39b0f6-b707-459a-9d7e-7e2625177b9f", | 315 | "id": "7c39b0f6-b707-459a-9d7e-7e2625177b9f", | ||
| 275 | "name": "indoor scenes", | 316 | "name": "indoor scenes", | ||
| 276 | "state": "active", | 317 | "state": "active", | ||
| 277 | "vocabulary_id": null | 318 | "vocabulary_id": null | ||
| 278 | }, | 319 | }, | ||
| 279 | { | 320 | { | ||
| 280 | "display_name": "sparse views", | 321 | "display_name": "sparse views", | ||
| 281 | "id": "a441a509-36db-4109-ba3f-e543c73fd95e", | 322 | "id": "a441a509-36db-4109-ba3f-e543c73fd95e", | ||
| 282 | "name": "sparse views", | 323 | "name": "sparse views", | ||
| 283 | "state": "active", | 324 | "state": "active", | ||
| 284 | "vocabulary_id": null | 325 | "vocabulary_id": null | ||
| 285 | }, | 326 | }, | ||
| 286 | { | 327 | { | ||
| 287 | "display_name": "volumetric reconstruction", | 328 | "display_name": "volumetric reconstruction", | ||
| 288 | "id": "b4d5a8db-555d-4554-a9e2-0aa4c135565b", | 329 | "id": "b4d5a8db-555d-4554-a9e2-0aa4c135565b", | ||
| 289 | "name": "volumetric reconstruction", | 330 | "name": "volumetric reconstruction", | ||
| 290 | "state": "active", | 331 | "state": "active", | ||
| 291 | "vocabulary_id": null | 332 | "vocabulary_id": null | ||
| 292 | } | 333 | } | ||
| 293 | ], | 334 | ], | ||
| 294 | "title": "NYUv2", | 335 | "title": "NYUv2", | ||
| 295 | "type": "dataset", | 336 | "type": "dataset", | ||
| 296 | "version": "" | 337 | "version": "" | ||
| 297 | } | 338 | } |