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Multimodal named entity recognition and relation extraction (MNER and MRE) isa fundamental and crucial branch in information extraction. However, existingapproaches for MNER and MRE usually suffer from error sensitivity whenirrelevant object images incorporated in texts. To deal with these issues, wepropose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) forvisual-enhanced entity and relation extraction, aiming to achieve moreeffective and robust performance. Specifically, we regard visual representationas pluggable visual prefix to guide the textual representation for errorinsensitive forecasting decision. We further propose a dynamic gatedaggregation strategy to achieve hierarchical multi-scaled visual features asvisual prefix for fusion. Extensive experiments on three benchmark datasetsdemonstrate the effectiveness of our method, and achieve state-of-the-artperformance. Code is available in https://github.com/zjunlp/HVPNeT.

Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction