Entity6K: A Large Open-Domain Evaluation Dataset for Real-World Entity
  Recognition
Jielin Qiu William Han Winfred Wang Zhengyuan Yang Linjie Li Jianfeng Wang Christos Faloutsos Lei Li Lijuan Wang
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.