  Recent advances in named entity recognition (NER) have pushed the boundary of
the task to incorporate visual signals, leading to many variants, including
multi-modal NER (MNER) or grounded MNER (GMNER). A key challenge to these tasks
is that the model should be able to generalize to the entities unseen during
the training, and should be able to handle the training samples with noisy
annotations. To address this obstacle, we propose SCANNER (Span CANdidate
detection and recognition for NER), a model capable of effectively handling all
three NER variants. SCANNER is a two-stage structure; we extract entity
candidates in the first stage and use it as a query to get knowledge,
effectively pulling knowledge from various sources. We can boost our
performance by utilizing this entity-centric extracted knowledge to address
unseen entities. Furthermore, to tackle the challenges arising from noisy
annotations in NER datasets, we introduce a novel self-distillation method,
enhancing the robustness and accuracy of our model in processing training data
with inherent uncertainties. Our approach demonstrates competitive performance
on the NER benchmark and surpasses existing methods on both MNER and GMNER
benchmarks. Further analysis shows that the proposed distillation and knowledge
utilization methods improve the performance of our model on various benchmarks.
