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  "author": "Eric M\u00fcller-Budack, Matthias Springstein, Sherzod 
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  "author": "Eric M\u00fcller-Budack, Matthias Springstein, Sherzod 
            
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Hakimov, Kevin Mrutzek, Ralph Ewerth",
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Hakimov, Kevin Mrutzek, Ralph Ewerth",
            
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  "author_email": "eric.mueller@tib.eu",
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  "author_email": "eric.mueller@tib.eu",
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "doi": "10.25835/0072909",
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  "doi": "10.25835/0072909",
            
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  "doi_date_published": "2020-11-11",
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  "doi_date_published": "2020-11-11",
            
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  "doi_publisher": "LUIS",
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  "doi_publisher": "LUIS",
            
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  "doi_status": "true",
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  "doi_status": "true",
            
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  "domain": "https://data.uni-hannover.de",
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  "domain": "https://data.uni-hannover.de",
            
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  "have_copyright": "Yes",
            
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  "license_id": "CC-BY-NC-3.0",
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  "license_id": "CC-BY-NC-3.0",
            
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  "license_title": "CC-BY-NC-3.0",
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  "license_title": "CC-BY-NC-3.0",
            
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  "maintainer": "Eric M\u00fcller-Budack",
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  "maintainer": "Eric M\u00fcller-Budack",
            
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  "maintainer_email": "eric.mueller@tib.eu",
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  "maintainer_email": "eric.mueller@tib.eu",
            
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  "name": "luh-vise",
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  "name": "luh-vise",
            
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  "notes": "# VisE-D: Visual Event Classification Dataset\r\n\r\nThis 
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  "notes": "# VisE-D: Visual Event Classification Dataset\r\n\r\nThis 
            
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repository contains the *Visual Event Classification Dataset (VisE-D)* 
23
repository contains the *Visual Event Classification Dataset (VisE-D)* 
            
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introduced in the paper:\r\n\r\n> Eric M\u00fcller-Budack, Matthias 
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introduced in the paper:\r\n\r\n> Eric M\u00fcller-Budack, Matthias 
            
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Springstein, Sherzod Hakimov, Kevin Mrutzek, and Ralph 
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Springstein, Sherzod Hakimov, Kevin Mrutzek, and Ralph 
            
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Ewerth:\r\n\"Ontology-driven Event Type Classification in 
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Ewerth:\r\n\"Ontology-driven Event Type Classification in 
            
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Images\".\r\nIn: *IEEE Winter Conference on Applications of Computer 
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Images\".\r\nIn: *IEEE Winter Conference on Applications of Computer 
            
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Vision (WACV)*, IEEE, 2021.\r\n\r\n\r\n## Content \r\n\r\n- 
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Vision (WACV)*, IEEE, 2021.\r\n\r\n\r\n## Content \r\n\r\n- 
            
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VisE-D.tar.gz:\r\n    - List of image URLs with corresponding meta 
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VisE-D.tar.gz:\r\n    - List of image URLs with corresponding meta 
            
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information for the:\r\n        - *VisE-D* train dataset\r\n        - 
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information for the:\r\n        - *VisE-D* train dataset\r\n        - 
            
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*VisE-D* validation dataset\r\n        - *VisE-Bing* test dataset\r\n        
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*VisE-D* validation dataset\r\n        - *VisE-Bing* test dataset\r\n        
            
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- *VisE-Bing* test dataset\r\n    - Different versions of the *Visual 
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- *VisE-Bing* test dataset\r\n    - Different versions of the *Visual 
            
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Event Ontology (VisE-O)*:\r\n        - Initial Ontology\r\n        - 
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Event Ontology (VisE-O)*:\r\n        - Initial Ontology\r\n        - 
            
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Disambiguated Ontology\r\n        - Disambigauted Ontology for *Event 
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Disambiguated Ontology\r\n        - Disambigauted Ontology for *Event 
            
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Nodes* related to a minimum of ten *Events*\r\n        - Refined 
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Nodes* related to a minimum of ten *Events*\r\n        - Refined 
            
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Ontology\r\n- VisE-C.tar.gz: Classification baseline (denoted as C) 
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Ontology\r\n- VisE-C.tar.gz: Classification baseline (denoted as C) 
            
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based on the ResNet-50 architecture trained on *VisE-D*.\r\n- 
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based on the ResNet-50 architecture trained on *VisE-D*.\r\n- 
            
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VisE-CO_cel.tar.gz: Ontology-driven approach with redundancy removal 
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VisE-CO_cel.tar.gz: Ontology-driven approach with redundancy removal 
            
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(denoted as CO^{cel}_{6\\omega}-RR) based on the ResNet-50 
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(denoted as CO^{cel}_{6\\omega}-RR) based on the ResNet-50 
            
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architecture trained on *VisE-D*.\r\n- VisE-CO_cos.tar.gz: 
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architecture trained on *VisE-D*.\r\n- VisE-CO_cos.tar.gz: 
            
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Ontology-driven approach (denoted as CO^{cos}_{\\delta}) based on the 
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Ontology-driven approach (denoted as CO^{cos}_{\\delta}) based on the 
            
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ResNet-50 architecture trained on *VisE-D*.\r\n- RED.tar.gz: 
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ResNet-50 architecture trained on *VisE-D*.\r\n- RED.tar.gz: 
            
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Ontologies and splits for the *RED* benchmark datasets (Ahsan et al. 
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Ontologies and splits for the *RED* benchmark datasets (Ahsan et al. 
            
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2017)\r\n- SocEID.tar.gz: Ontologies and splits for the *SocEID* 
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2017)\r\n- SocEID.tar.gz: Ontologies and splits for the *SocEID* 
            
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benchmark datasets (Ahsan et al. 2017)\r\n- WIDER.tar.gz: Ontologies 
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benchmark datasets (Ahsan et al. 2017)\r\n- WIDER.tar.gz: Ontologies 
            
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and splits for the *WIDER* benchmark datasets (Xiong et al. 2015) 
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and splits for the *WIDER* benchmark datasets (Xiong et al. 2015) 
            
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\r\n\r\n\r\n## Source Code\r\n\r\nThe source code to reproduce our 
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\r\n\r\n\r\n## Source Code\r\n\r\nThe source code to reproduce our 
            
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results can be found on our GitHub page: 
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results can be found on our GitHub page: 
            
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https://github.com/TIBHannover/VisE",
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