  Multi-Modal Relation Extraction (MMRE) aims at identifying the relation
between two entities in texts that contain visual clues. Rich visual content is
valuable for the MMRE task, but existing works cannot well model finer
associations among different modalities, failing to capture the truly helpful
visual information and thus limiting relation extraction performance. In this
paper, we propose a novel MMRE framework to better capture the deeper
correlations of text, entity pair, and image/objects, so as to mine more
helpful information for the task, termed as DGF-PT. We first propose a
prompt-based autoregressive encoder, which builds the associations of
intra-modal and inter-modal features related to the task, respectively by
entity-oriented and object-oriented prefixes. To better integrate helpful
visual information, we design a dual-gated fusion module to distinguish the
importance of image/objects and further enrich text representations. In
addition, a generative decoder is introduced with entity type restriction on
relations, better filtering out candidates. Extensive experiments conducted on
the benchmark dataset show that our approach achieves excellent performance
compared to strong competitors, even in the few-shot situation.
