VP-MEL: Visual Prompts Guided Multimodal Entity Linking

TL;DR

This paper introduces VP-MEL, a novel multimodal entity linking task guided by visual prompts, along with a new dataset and a framework that improves entity retrieval by better utilizing visual information.

Contribution

The paper proposes the VP-MEL task, creates the VPWiki dataset, and develops the IIER framework that enhances visual feature extraction for improved multimodal entity linking.

Findings

IIER outperforms baseline methods on VPWiki

Visual prompts improve entity linking accuracy

New dataset VPWiki facilitates VP-MEL research

Abstract

Multimodal entity linking (MEL), a task aimed at linking mentions within multimodal contexts to their corresponding entities in a knowledge base (KB), has attracted much attention due to its wide applications in recent years. However, existing MEL methods often rely on mention words as retrieval cues, which limits their ability to effectively utilize information from both images and text. This reliance causes MEL to struggle with accurately retrieving entities in certain scenarios, especially when the focus is on image objects or mention words are missing from the text. To solve these issues, we introduce a Visual Prompts guided Multimodal Entity Linking (VP-MEL) task. Given a text-image pair, VP-MEL aims to link a marked region (i.e., visual prompt) in an image to its corresponding entities in the knowledge base. To facilitate this task, we present a new dataset, VPWiki, specifically designed for VP-MEL. Furthermore, we propose a framework named IIER, which enhances visual feature extraction using visual prompts and leverages the pretrained Detective-VLM model to capture latent information. Experimental results on the VPWiki dataset demonstrate that IIER outperforms baseline methods across multiple benchmarks for the VP-MEL task.