Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering

TL;DR

This paper introduces a multi-modal keyphrase generation model that enhances input with external visual entities and filters image noise through multi-granularity scoring, achieving state-of-the-art results.

Contribution

It proposes a novel approach combining external visual knowledge and multi-granularity noise filtering to improve multi-modal keyphrase generation.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively filters image noise using correlation scores.

Enriches input with external visual entities for better semantic alignment.

Abstract

Multi-modal keyphrase generation aims to produce a set of keyphrases that represent the core points of the input text-image pair. In this regard, dominant methods mainly focus on multi-modal fusion for keyphrase generation. Nevertheless, there are still two main drawbacks: 1) only a limited number of sources, such as image captions, can be utilized to provide auxiliary information. However, they may not be sufficient for the subsequent keyphrase generation. 2) the input text and image are often not perfectly matched, and thus the image may introduce noise into the model. To address these limitations, in this paper, we propose a novel multi-modal keyphrase generation model, which not only enriches the model input with external knowledge, but also effectively filters image noise. First, we introduce external visual entities of the image as the supplementary input to the model, which benefits the cross-modal semantic alignment for keyphrase generation. Second, we simultaneously calculate an image-text matching score and image region-text correlation scores to perform multi-granularity image noise filtering. Particularly, we introduce the correlation scores between image regions and ground-truth keyphrases to refine the calculation of the previously-mentioned correlation scores. To demonstrate the effectiveness of our model, we conduct several groups of experiments on the benchmark dataset. Experimental results and in-depth analyses show that our model achieves the state-of-the-art performance. Our code is available on https://github.com/DeepLearnXMU/MM-MKP.