Grounding Language Models for Visual Entity Recognition

• URL: http://arxiv.org/abs/2402.18695v2
• Date: Fri, 26 Jul 2024 06:34:15 GMT
• Title: Grounding Language Models for Visual Entity Recognition
• Authors: Zilin Xiao, Ming Gong, Paola Cascante-Bonilla, Xingyao Zhang, Jie Wu, Vicente Ordonez,
• Abstract summary: AutoVER is an autoregressive model for Visual Entity Recognition. It mitigates low performance on out-of-domain entities while excelling in queries that require visually-situated reasoning. It achieves significant improvements across different dataset splits in the recently proposed Oven-Wiki benchmark.
• Score: 27.783314622717448
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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