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On December 3, 2024 at 11:04:39 AM UTC, admin:
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Changed value of field
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in Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering -
Changed value of field
doi_date_published
to2024-12-03
in Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering -
Added resource Original Metadata to Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering
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2 | "access_rights": "", | 2 | "access_rights": "", | ||
3 | "author": "Ruixue Tang", | 3 | "author": "Ruixue Tang", | ||
4 | "author_email": "", | 4 | "author_email": "", | ||
5 | "citation": [], | 5 | "citation": [], | ||
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13 | "extra_authors": [ | 13 | "extra_authors": [ | ||
14 | { | 14 | { | ||
15 | "extra_author": "Chao Ma", | 15 | "extra_author": "Chao Ma", | ||
16 | "orcid": "" | 16 | "orcid": "" | ||
17 | }, | 17 | }, | ||
18 | { | 18 | { | ||
19 | "extra_author": "Wei Emma Zhang", | 19 | "extra_author": "Wei Emma Zhang", | ||
20 | "orcid": "" | 20 | "orcid": "" | ||
21 | }, | 21 | }, | ||
22 | { | 22 | { | ||
23 | "extra_author": "Qi Wu", | 23 | "extra_author": "Qi Wu", | ||
24 | "orcid": "" | 24 | "orcid": "" | ||
25 | }, | 25 | }, | ||
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27 | "extra_author": "Xiaokang Yang", | 27 | "extra_author": "Xiaokang Yang", | ||
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33 | "description": "", | 33 | "description": "", | ||
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38 | "title": "Visual Question Answering" | 38 | "title": "Visual Question Answering" | ||
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43 | "landing_page": "https://github.com/zaynmi/seada-vqa", | 43 | "landing_page": "https://github.com/zaynmi/seada-vqa", | ||
44 | "license_title": null, | 44 | "license_title": null, | ||
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46 | "metadata_created": "2024-12-03T11:04:37.838647", | 46 | "metadata_created": "2024-12-03T11:04:37.838647", | ||
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48 | "name": | 48 | "name": | ||
49 | uivalent-adversarial-data-augmentation-for-visual-question-answering", | 49 | uivalent-adversarial-data-augmentation-for-visual-question-answering", | ||
50 | "notes": "Visual Question Answering (VQA) has achieved great success | 50 | "notes": "Visual Question Answering (VQA) has achieved great success | ||
51 | thanks to the fast development of deep neural networks (DNN). On the | 51 | thanks to the fast development of deep neural networks (DNN). On the | ||
52 | other hand, the data augmentation, as one of the major tricks for DNN, | 52 | other hand, the data augmentation, as one of the major tricks for DNN, | ||
53 | has been widely used in many computer vision tasks.", | 53 | has been widely used in many computer vision tasks.", | ||
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55 | "num_tags": 3, | 55 | "num_tags": 3, | ||
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57 | "approval_status": "approved", | 57 | "approval_status": "approved", | ||
58 | "created": "2024-11-25T12:11:38.292601", | 58 | "created": "2024-11-25T12:11:38.292601", | ||
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71 | "relationships_as_subject": [], | 71 | "relationships_as_subject": [], | ||
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74 | "state": "active", | 115 | "state": "active", | ||
75 | "tags": [ | 116 | "tags": [ | ||
76 | { | 117 | { | ||
77 | "display_name": "Adversarial Examples", | 118 | "display_name": "Adversarial Examples", | ||
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83 | { | 124 | { | ||
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90 | { | 131 | { | ||
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96 | } | 137 | } | ||
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98 | "title": "Semantic Equivalent Adversarial Data Augmentation for | 139 | "title": "Semantic Equivalent Adversarial Data Augmentation for | ||
99 | Visual Question Answering", | 140 | Visual Question Answering", | ||
100 | "type": "dataset", | 141 | "type": "dataset", | ||
101 | "version": "" | 142 | "version": "" | ||
102 | } | 143 | } |