Changes
On December 2, 2024 at 11:00:34 PM UTC, admin:
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Changed title to PASCAL Context (previously Pascal Context)
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Updated description of PASCAL Context from
Semantic segmentation is a crucial and challenging task for image understanding. It aims to predict a dense labeling map for the input image, which assigns each pixel a unique category label.
toThe PASCAL Context dataset is a benchmark for multi-task learning in computer vision. It contains 10103 images with 5 tasks: semantic segmentation, human body part segmentation, surface normal estimation, saliency estimation, and edge detection.
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Removed the following tags from PASCAL Context
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Added the following tags to PASCAL Context
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Changed value of field
defined_in
tohttps://doi.org/10.48550/arXiv.2003.10211
in PASCAL Context -
Changed value of field
extra_authors
to[{'extra_author': 'X. Chen', 'orcid': ''}, {'extra_author': 'X. Liu', 'orcid': ''}, {'extra_author': 'N. Cho', 'orcid': ''}, {'extra_author': 'S. Lee', 'orcid': ''}, {'extra_author': 'S. Fidler', 'orcid': ''}, {'extra_author': 'R. Urtasun', 'orcid': ''}, {'extra_author': 'A. Yuille', 'orcid': ''}]
in PASCAL Context -
Changed value of field
citation
to['https://doi.org/10.48550/arXiv.2304.06957', 'https://doi.org/10.48550/arXiv.1911.07257', 'https://doi.org/10.48550/arXiv.2312.13514', 'https://doi.org/10.48550/arXiv.2108.06536', 'https://doi.org/10.48550/arXiv.2002.07371', 'https://doi.org/10.48550/arXiv.1701.07122']
in PASCAL Context -
Deleted resource Original Metadata from PASCAL Context
f | 1 | { | f | 1 | { |
2 | "access_rights": "", | 2 | "access_rights": "", | ||
3 | "author": "R. Mottaghi", | 3 | "author": "R. Mottaghi", | ||
4 | "author_email": "", | 4 | "author_email": "", | ||
5 | "citation": [ | 5 | "citation": [ | ||
n | 6 | "https://doi.org/10.48550/arXiv.1903.04688", | n | 6 | "https://doi.org/10.48550/arXiv.2304.06957", |
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8 | "https://doi.org/10.48550/arXiv.1611.08986", | ||||
9 | "https://doi.org/10.48550/arXiv.2312.11872" | 8 | "https://doi.org/10.48550/arXiv.2312.13514", | ||
9 | "https://doi.org/10.48550/arXiv.2108.06536", | ||||
10 | "https://doi.org/10.48550/arXiv.2002.07371", | ||||
11 | "https://doi.org/10.48550/arXiv.1701.07122" | ||||
10 | ], | 12 | ], | ||
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n | 12 | "defined_in": "https://doi.org/10.48550/arXiv.2406.09936", | n | 14 | "defined_in": "https://doi.org/10.48550/arXiv.2003.10211", |
13 | "doi": "10.57702/i5758bop", | 15 | "doi": "10.57702/i5758bop", | ||
14 | "doi_date_published": "2024-12-02", | 16 | "doi_date_published": "2024-12-02", | ||
15 | "doi_publisher": "TIB", | 17 | "doi_publisher": "TIB", | ||
16 | "doi_status": true, | 18 | "doi_status": true, | ||
17 | "domain": "https://service.tib.eu/ldmservice", | 19 | "domain": "https://service.tib.eu/ldmservice", | ||
18 | "extra_authors": [ | 20 | "extra_authors": [ | ||
19 | { | 21 | { | ||
20 | "extra_author": "X. Chen", | 22 | "extra_author": "X. Chen", | ||
21 | "orcid": "" | 23 | "orcid": "" | ||
22 | }, | 24 | }, | ||
23 | { | 25 | { | ||
24 | "extra_author": "X. Liu", | 26 | "extra_author": "X. Liu", | ||
25 | "orcid": "" | 27 | "orcid": "" | ||
26 | }, | 28 | }, | ||
27 | { | 29 | { | ||
n | 28 | "extra_author": "N.-G. Cho", | n | 30 | "extra_author": "N. Cho", |
29 | "orcid": "" | 31 | "orcid": "" | ||
30 | }, | 32 | }, | ||
31 | { | 33 | { | ||
n | 32 | "extra_author": "S.-W. Lee", | n | 34 | "extra_author": "S. Lee", |
33 | "orcid": "" | 35 | "orcid": "" | ||
34 | }, | 36 | }, | ||
35 | { | 37 | { | ||
36 | "extra_author": "S. Fidler", | 38 | "extra_author": "S. Fidler", | ||
37 | "orcid": "" | 39 | "orcid": "" | ||
38 | }, | 40 | }, | ||
39 | { | 41 | { | ||
40 | "extra_author": "R. Urtasun", | 42 | "extra_author": "R. Urtasun", | ||
41 | "orcid": "" | 43 | "orcid": "" | ||
42 | }, | 44 | }, | ||
43 | { | 45 | { | ||
44 | "extra_author": "A. Yuille", | 46 | "extra_author": "A. Yuille", | ||
45 | "orcid": "" | 47 | "orcid": "" | ||
46 | } | 48 | } | ||
47 | ], | 49 | ], | ||
48 | "groups": [ | 50 | "groups": [ | ||
49 | { | 51 | { | ||
50 | "description": "", | 52 | "description": "", | ||
n | 51 | "display_name": "Context-Aware Image Segmentation", | n | 53 | "display_name": "Computer Vision", |
52 | "id": "df0e8a79-e6e7-4e64-b4b4-aba331ed1340", | 54 | "id": "d09caf7c-26c7-4e4d-bb8e-49476a90ba25", | ||
53 | "image_display_url": "", | 55 | "image_display_url": "", | ||
n | 54 | "name": "context-aware-image-segmentation", | n | 56 | "name": "computer-vision", |
55 | "title": "Context-Aware Image Segmentation" | 57 | "title": "Computer Vision" | ||
56 | }, | ||||
57 | { | ||||
58 | "description": "", | ||||
59 | "display_name": "Image Dataset", | ||||
60 | "id": "fc745cca-b21e-4ced-ba81-06a456938edf", | ||||
61 | "image_display_url": "", | ||||
62 | "name": "image-dataset", | ||||
63 | "title": "Image Dataset" | ||||
64 | }, | 58 | }, | ||
65 | { | 59 | { | ||
66 | "description": "", | 60 | "description": "", | ||
67 | "display_name": "Image Segmentation", | 61 | "display_name": "Image Segmentation", | ||
68 | "id": "7c8cc5f1-a9b2-4924-82ec-9e3aa3049a04", | 62 | "id": "7c8cc5f1-a9b2-4924-82ec-9e3aa3049a04", | ||
69 | "image_display_url": "", | 63 | "image_display_url": "", | ||
70 | "name": "image-segmentation", | 64 | "name": "image-segmentation", | ||
71 | "title": "Image Segmentation" | 65 | "title": "Image Segmentation" | ||
72 | }, | 66 | }, | ||
73 | { | 67 | { | ||
74 | "description": "", | 68 | "description": "", | ||
n | 75 | "display_name": "Natural Scene Understanding", | n | 69 | "display_name": "Multi-task Learning", |
76 | "id": "ee36a499-a58b-4498-bcae-3ec4b0ec9a99", | 70 | "id": "323beab8-de4e-4bbd-a95c-94628c827cfe", | ||
77 | "image_display_url": "", | 71 | "image_display_url": "", | ||
n | 78 | "name": "natural-scene-understanding", | n | 72 | "name": "multi-task-learning", |
79 | "title": "Natural Scene Understanding" | 73 | "title": "Multi-task Learning" | ||
80 | }, | 74 | }, | ||
81 | { | 75 | { | ||
82 | "description": "", | 76 | "description": "", | ||
83 | "display_name": "Object Detection", | 77 | "display_name": "Object Detection", | ||
84 | "id": "ca2cb1af-d31c-49b0-a1dd-62b22f2b9e20", | 78 | "id": "ca2cb1af-d31c-49b0-a1dd-62b22f2b9e20", | ||
85 | "image_display_url": "", | 79 | "image_display_url": "", | ||
86 | "name": "object-detection", | 80 | "name": "object-detection", | ||
87 | "title": "Object Detection" | 81 | "title": "Object Detection" | ||
n | n | 82 | }, | ||
83 | { | ||||
84 | "description": "", | ||||
85 | "display_name": "Semantic Image Segmentation", | ||||
86 | "id": "158ad1b6-c106-4fea-9699-a66141bd23b6", | ||||
87 | "image_display_url": "", | ||||
88 | "name": "semantic-image-segmentation", | ||||
89 | "title": "Semantic Image Segmentation" | ||||
88 | }, | 90 | }, | ||
89 | { | 91 | { | ||
90 | "description": "", | 92 | "description": "", | ||
91 | "display_name": "Semantic Segmentation", | 93 | "display_name": "Semantic Segmentation", | ||
92 | "id": "8c3f2eee-f5f9-464d-9c0a-1a5e7a925c0e", | 94 | "id": "8c3f2eee-f5f9-464d-9c0a-1a5e7a925c0e", | ||
93 | "image_display_url": "", | 95 | "image_display_url": "", | ||
94 | "name": "semantic-segmentation", | 96 | "name": "semantic-segmentation", | ||
95 | "title": "Semantic Segmentation" | 97 | "title": "Semantic Segmentation" | ||
96 | } | 98 | } | ||
97 | ], | 99 | ], | ||
98 | "id": "da2cc808-18c6-435e-bad8-d3c3bc742ebb", | 100 | "id": "da2cc808-18c6-435e-bad8-d3c3bc742ebb", | ||
99 | "isopen": false, | 101 | "isopen": false, | ||
100 | "landing_page": "https://www.pascal-voc.org/", | 102 | "landing_page": "https://www.pascal-voc.org/", | ||
101 | "license_title": null, | 103 | "license_title": null, | ||
102 | "link_orkg": "", | 104 | "link_orkg": "", | ||
103 | "metadata_created": "2024-12-02T22:15:11.871720", | 105 | "metadata_created": "2024-12-02T22:15:11.871720", | ||
n | 104 | "metadata_modified": "2024-12-02T22:35:57.891297", | n | 106 | "metadata_modified": "2024-12-02T23:00:33.506250", |
105 | "name": "pascal-context", | 107 | "name": "pascal-context", | ||
n | 106 | "notes": "Semantic segmentation is a crucial and challenging task | n | 108 | "notes": "The PASCAL Context dataset is a benchmark for multi-task |
107 | for image understanding. It aims to predict a dense labeling map for | 109 | learning in computer vision. It contains 10103 images with 5 tasks: | ||
108 | the input image, which assigns each pixel a unique category label.", | 110 | semantic segmentation, human body part segmentation, surface normal | ||
111 | estimation, saliency estimation, and edge detection.", | ||||
109 | "num_resources": 1, | 112 | "num_resources": 0, | ||
110 | "num_tags": 14, | 113 | "num_tags": 13, | ||
111 | "organization": { | 114 | "organization": { | ||
112 | "approval_status": "approved", | 115 | "approval_status": "approved", | ||
113 | "created": "2024-11-25T12:11:38.292601", | 116 | "created": "2024-11-25T12:11:38.292601", | ||
114 | "description": "", | 117 | "description": "", | ||
115 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 118 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
116 | "image_url": "", | 119 | "image_url": "", | ||
117 | "is_organization": true, | 120 | "is_organization": true, | ||
118 | "name": "no-organization", | 121 | "name": "no-organization", | ||
119 | "state": "active", | 122 | "state": "active", | ||
120 | "title": "No Organization", | 123 | "title": "No Organization", | ||
121 | "type": "organization" | 124 | "type": "organization" | ||
122 | }, | 125 | }, | ||
123 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 126 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
124 | "private": false, | 127 | "private": false, | ||
125 | "relationships_as_object": [], | 128 | "relationships_as_object": [], | ||
126 | "relationships_as_subject": [], | 129 | "relationships_as_subject": [], | ||
n | 127 | "resources": [ | n | 130 | "resources": [], |
128 | { | ||||
129 | "cache_last_updated": null, | ||||
130 | "cache_url": null, | ||||
131 | "created": "2024-12-02T22:29:38", | ||||
132 | "data": [ | ||||
133 | "dcterms:title", | ||||
134 | "dcterms:accessRights", | ||||
135 | "dcterms:creator", | ||||
136 | "dcterms:description", | ||||
137 | "dcterms:issued", | ||||
138 | "dcterms:language", | ||||
139 | "dcterms:identifier", | ||||
140 | "dcat:theme", | ||||
141 | "dcterms:type", | ||||
142 | "dcat:keyword", | ||||
143 | "dcat:landingPage", | ||||
144 | "dcterms:hasVersion", | ||||
145 | "dcterms:format", | ||||
146 | "mls:task", | ||||
147 | "datacite:isDescribedBy" | ||||
148 | ], | ||||
149 | "description": "The json representation of the dataset with its | ||||
150 | distributions based on DCAT.", | ||||
151 | "format": "JSON", | ||||
152 | "hash": "", | ||||
153 | "id": "b7524332-c649-4b2b-b84a-6a26af7c746a", | ||||
154 | "last_modified": "2024-12-02T22:35:57.882280", | ||||
155 | "metadata_modified": "2024-12-02T22:35:57.894134", | ||||
156 | "mimetype": "application/json", | ||||
157 | "mimetype_inner": null, | ||||
158 | "name": "Original Metadata", | ||||
159 | "package_id": "da2cc808-18c6-435e-bad8-d3c3bc742ebb", | ||||
160 | "position": 0, | ||||
161 | "resource_type": null, | ||||
162 | "size": 1402, | ||||
163 | "state": "active", | ||||
164 | "url": | ||||
165 | resource/b7524332-c649-4b2b-b84a-6a26af7c746a/download/metadata.json", | ||||
166 | "url_type": "upload" | ||||
167 | } | ||||
168 | ], | ||||
169 | "services_used_list": "", | 131 | "services_used_list": "", | ||
170 | "state": "active", | 132 | "state": "active", | ||
171 | "tags": [ | 133 | "tags": [ | ||
172 | { | 134 | { | ||
n | 173 | "display_name": "Dataset", | n | 135 | "display_name": "Edge Detection", |
174 | "id": "81587eb2-9569-4a4b-83c8-0e2ac78e7e3b", | 136 | "id": "316a5620-f93f-4b57-bd98-1e78a33cde79", | ||
137 | "name": "Edge Detection", | ||||
175 | "name": "Dataset", | 138 | "state": "active", | ||
176 | "state": "active", | ||||
177 | "vocabulary_id": null | 139 | "vocabulary_id": null | ||
178 | }, | 140 | }, | ||
179 | { | 141 | { | ||
n | 180 | "display_name": "Image", | n | 142 | "display_name": "Human Body Part Segmentation", |
181 | "id": "ca701146-4139-43fe-a114-4f31ab3d20a1", | 143 | "id": "f0d034c7-56d1-4c84-b9cd-9902dc289430", | ||
182 | "name": "Image", | 144 | "name": "Human Body Part Segmentation", | ||
183 | "state": "active", | 145 | "state": "active", | ||
184 | "vocabulary_id": null | 146 | "vocabulary_id": null | ||
185 | }, | 147 | }, | ||
186 | { | 148 | { | ||
187 | "display_name": "Image Segmentation", | 149 | "display_name": "Image Segmentation", | ||
188 | "id": "f5603951-aef2-4539-8066-15e72f32271b", | 150 | "id": "f5603951-aef2-4539-8066-15e72f32271b", | ||
189 | "name": "Image Segmentation", | 151 | "name": "Image Segmentation", | ||
190 | "state": "active", | 152 | "state": "active", | ||
191 | "vocabulary_id": null | 153 | "vocabulary_id": null | ||
192 | }, | 154 | }, | ||
193 | { | 155 | { | ||
n | n | 156 | "display_name": "Object Detection", | ||
157 | "id": "44adc011-570b-46cf-9a65-ab72ca690477", | ||||
158 | "name": "Object Detection", | ||||
159 | "state": "active", | ||||
160 | "vocabulary_id": null | ||||
161 | }, | ||||
162 | { | ||||
194 | "display_name": "Pascal Context", | 163 | "display_name": "PASCAL Context", | ||
195 | "id": "94ed303c-8256-498d-a281-9a857ffd97c8", | 164 | "id": "45dc6fe5-367a-41a8-9d3e-aa6fb78ab8a7", | ||
196 | "name": "Pascal Context", | 165 | "name": "PASCAL Context", | ||
166 | "state": "active", | ||||
167 | "vocabulary_id": null | ||||
168 | }, | ||||
169 | { | ||||
170 | "display_name": "Saliency Estimation", | ||||
171 | "id": "4f20a91b-76ce-4fd8-b2de-27d95c2cca8f", | ||||
172 | "name": "Saliency Estimation", | ||||
173 | "state": "active", | ||||
174 | "vocabulary_id": null | ||||
175 | }, | ||||
176 | { | ||||
177 | "display_name": "Segmentation", | ||||
178 | "id": "afba543e-f91f-4800-834e-77535c9e8dac", | ||||
179 | "name": "Segmentation", | ||||
197 | "state": "active", | 180 | "state": "active", | ||
198 | "vocabulary_id": null | 181 | "vocabulary_id": null | ||
199 | }, | 182 | }, | ||
200 | { | 183 | { | ||
201 | "display_name": "Semantic Segmentation", | 184 | "display_name": "Semantic Segmentation", | ||
202 | "id": "809ad6af-28cd-43bd-974d-055a5c0f2973", | 185 | "id": "809ad6af-28cd-43bd-974d-055a5c0f2973", | ||
203 | "name": "Semantic Segmentation", | 186 | "name": "Semantic Segmentation", | ||
204 | "state": "active", | 187 | "state": "active", | ||
205 | "vocabulary_id": null | 188 | "vocabulary_id": null | ||
206 | }, | 189 | }, | ||
207 | { | 190 | { | ||
n | 208 | "display_name": "context-aware image segmentation", | n | 191 | "display_name": "Surface Normal Estimation", |
209 | "id": "91d5ecb9-b757-46bf-a3d9-9e269c06071a", | 192 | "id": "b9a6d8c0-2eda-4bc1-b459-207b2c1b5622", | ||
210 | "name": "context-aware image segmentation", | 193 | "name": "Surface Normal Estimation", | ||
211 | "state": "active", | ||||
212 | "vocabulary_id": null | ||||
213 | }, | ||||
214 | { | ||||
215 | "display_name": "image classification", | ||||
216 | "id": "34936550-ce1a-41b5-8c58-23081a6c673d", | ||||
217 | "name": "image classification", | ||||
218 | "state": "active", | ||||
219 | "vocabulary_id": null | ||||
220 | }, | ||||
221 | { | ||||
222 | "display_name": "image dataset", | ||||
223 | "id": "d3acafab-ad07-46a1-88d5-540c2fd41466", | ||||
224 | "name": "image dataset", | ||||
225 | "state": "active", | ||||
226 | "vocabulary_id": null | ||||
227 | }, | ||||
228 | { | ||||
229 | "display_name": "image labeling", | ||||
230 | "id": "b077815b-61b5-4815-98d2-0fe21e556fd4", | ||||
231 | "name": "image labeling", | ||||
232 | "state": "active", | 194 | "state": "active", | ||
233 | "vocabulary_id": null | 195 | "vocabulary_id": null | ||
234 | }, | 196 | }, | ||
235 | { | 197 | { | ||
236 | "display_name": "image segmentation", | 198 | "display_name": "image segmentation", | ||
237 | "id": "7eaed78e-c73a-4929-a8c9-60265069f59a", | 199 | "id": "7eaed78e-c73a-4929-a8c9-60265069f59a", | ||
238 | "name": "image segmentation", | 200 | "name": "image segmentation", | ||
239 | "state": "active", | 201 | "state": "active", | ||
240 | "vocabulary_id": null | 202 | "vocabulary_id": null | ||
241 | }, | 203 | }, | ||
242 | { | 204 | { | ||
n | 243 | "display_name": "natural scene", | n | ||
244 | "id": "814bc820-4a10-4347-a61a-39d9f590f583", | ||||
245 | "name": "natural scene", | ||||
246 | "state": "active", | ||||
247 | "vocabulary_id": null | ||||
248 | }, | ||||
249 | { | ||||
250 | "display_name": "object detection", | 205 | "display_name": "object detection", | ||
251 | "id": "607283c7-9e12-4167-9101-7f8078fb6537", | 206 | "id": "607283c7-9e12-4167-9101-7f8078fb6537", | ||
252 | "name": "object detection", | 207 | "name": "object detection", | ||
253 | "state": "active", | 208 | "state": "active", | ||
254 | "vocabulary_id": null | 209 | "vocabulary_id": null | ||
255 | }, | 210 | }, | ||
256 | { | 211 | { | ||
n | n | 212 | "display_name": "pascal context", | ||
213 | "id": "a5068474-6385-49f4-a5ca-02658db44f60", | ||||
214 | "name": "pascal context", | ||||
215 | "state": "active", | ||||
216 | "vocabulary_id": null | ||||
217 | }, | ||||
218 | { | ||||
257 | "display_name": "semantic segmentation", | 219 | "display_name": "semantic segmentation", | ||
258 | "id": "f9237911-e9df-4dd5-a9aa-301b6d4969af", | 220 | "id": "f9237911-e9df-4dd5-a9aa-301b6d4969af", | ||
259 | "name": "semantic segmentation", | 221 | "name": "semantic segmentation", | ||
260 | "state": "active", | 222 | "state": "active", | ||
261 | "vocabulary_id": null | 223 | "vocabulary_id": null | ||
n | 262 | }, | n | ||
263 | { | ||||
264 | "display_name": "transformers", | ||||
265 | "id": "de8ae43b-acd0-4152-8c68-d20cb235cd5f", | ||||
266 | "name": "transformers", | ||||
267 | "state": "active", | ||||
268 | "vocabulary_id": null | ||||
269 | } | 224 | } | ||
270 | ], | 225 | ], | ||
t | 271 | "title": "Pascal Context", | t | 226 | "title": "PASCAL Context", |
272 | "type": "dataset", | 227 | "type": "dataset", | ||
273 | "version": "" | 228 | "version": "" | ||
274 | } | 229 | } |