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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2403.12339 |
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| _version_ | 1866929282043871232 |
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| author | Qiu, Jielin Han, William Wang, Winfred Yang, Zhengyuan Li, Linjie Wang, Jianfeng Faloutsos, Christos Li, Lei Wang, Lijuan |
| author_facet | Qiu, Jielin Han, William Wang, Winfred Yang, Zhengyuan Li, Linjie Wang, Jianfeng Faloutsos, Christos Li, Lei Wang, Lijuan |
| contents | Open-domain real-world entity recognition is essential yet challenging, involving identifying various entities in diverse environments. The lack of a suitable evaluation dataset has been a major obstacle in this field due to the vast number of entities and the extensive human effort required for data curation. We introduce Entity6K, a comprehensive dataset for real-world entity recognition, featuring 5,700 entities across 26 categories, each supported by 5 human-verified images with annotations. Entity6K offers a diverse range of entity names and categorizations, addressing a gap in existing datasets. We conducted benchmarks with existing models on tasks like image captioning, object detection, zero-shot classification, and dense captioning to demonstrate Entity6K's effectiveness in evaluating models' entity recognition capabilities. We believe Entity6K will be a valuable resource for advancing accurate entity recognition in open-domain settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12339 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Entity6K: A Large Open-Domain Evaluation Dataset for Real-World Entity Recognition Qiu, Jielin Han, William Wang, Winfred Yang, Zhengyuan Li, Linjie Wang, Jianfeng Faloutsos, Christos Li, Lei Wang, Lijuan Computer Vision and Pattern Recognition Open-domain real-world entity recognition is essential yet challenging, involving identifying various entities in diverse environments. The lack of a suitable evaluation dataset has been a major obstacle in this field due to the vast number of entities and the extensive human effort required for data curation. We introduce Entity6K, a comprehensive dataset for real-world entity recognition, featuring 5,700 entities across 26 categories, each supported by 5 human-verified images with annotations. Entity6K offers a diverse range of entity names and categorizations, addressing a gap in existing datasets. We conducted benchmarks with existing models on tasks like image captioning, object detection, zero-shot classification, and dense captioning to demonstrate Entity6K's effectiveness in evaluating models' entity recognition capabilities. We believe Entity6K will be a valuable resource for advancing accurate entity recognition in open-domain settings. |
| title | Entity6K: A Large Open-Domain Evaluation Dataset for Real-World Entity Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.12339 |