Entity6K: A Large Open-Domain Evaluation Dataset for Real-World Entity Recognition
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 42 references that are in the map. First posted on arXiv 19 Mar 2024.
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References · 28
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
- 2023Segment Anythingcited by 8,234
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
- 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
- 2017Prototypical Networks for Few-shot Learningcited by 3,015
- 2016Matching Networks for One Shot Learningcited by 2,255
16 more
- 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,319
- 2021Grounded Language-Image Pre-trainingcited by 1,156
- 2019Large-Scale Long-Tailed Recognition in an Open Worldcited by 705
- 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 544
- 2022GIT: A Generative Image-to-text Transformer for Vision and Languagecited by 537
- 2018Meta-Learning with Latent Embedding Optimizationcited by 476
- 2018nocaps: novel object captioning at scalecited by 444
- 2022BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizerscited by 303
- 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 294
- 2022GLIPv2: Unifying Localization and Vision-Language Understandingcited by 275
- 2023Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?cited by 207
- 2015DenseCap: Fully Convolutional Localization Networks for Dense Captioningcited by 176
- 2022GRiT: A Generative Region-to-text Transformer for Object Understandingcited by 121
- 2023Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entitiescited by 113
- 2022GRIT: Faster and Better Image captioning Transformer Using Dual Visual Featurescited by 56
- 2024SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLMcited by 18
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