Multimodal entity linking models are vulnerable to visual adversarial perturbations, and the proposed retrieval-augmented LLM method (LLM-RetLink) reportedly improves accuracy by 0.4% to 35.7%.
Entity6K: A Large Open-Domain Evaluation Dataset for Real-World Entity Recognition
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abstract
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.
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cs.IR 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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On Evaluating the Adversarial Robustness of Foundation Models for Multimodal Entity Linking
Multimodal entity linking models are vulnerable to visual adversarial perturbations, and the proposed retrieval-augmented LLM method (LLM-RetLink) reportedly improves accuracy by 0.4% to 35.7%.