Grounding Language Models for Visual Entity Recognition
Zilin Xiao Ming Gong Paola Cascante-Bonilla Xingyao Zhang Jie Wu Vicente Ordonez
We introduce AutoVER, an Autoregressive model for Visual Entity Recognition.
Our model extends an autoregressive Multi-modal Large Language Model by
employing retrieval augmented constrained generation. It mitigates low
performance on out-of-domain entities while excelling in queries that require
visually-situated reasoning. Our method learns to distinguish similar entities
within a vast label space by contrastively training on hard negative pairs in
parallel with a sequence-to-sequence objective without an external retriever.
During inference, a list of retrieved candidate answers explicitly guides
language generation by removing invalid decoding paths. The proposed method
achieves significant improvements across different dataset splits in the
recently proposed Oven-Wiki benchmark. Accuracy on the Entity seen split rises
from 32.7% to 61.5%. It also demonstrates superior performance on the unseen
and query splits by a substantial double-digit margin.