Visual Word Sense Disambiguation (VWSD) is a task to find the image that most
accurately depicts the correct sense of the target word for the given context.
Previously, image-text matching models often suffered from recognizing
polysemous words. This paper introduces an unsupervised VWSD approach that uses
gloss information of an external lexical knowledge-base, especially the sense
definitions. Specifically, we suggest employing Bayesian inference to
incorporate the sense definitions when sense information of the answer is not
provided. In addition, to ameliorate the out-of-dictionary (OOD) issue, we
propose a context-aware definition generation with GPT-3. Experimental results
show that the VWSD performance significantly increased with our Bayesian
inference-based approach. In addition, our context-aware definition generation
achieved prominent performance improvement in OOD examples exhibiting better
performance than the existing definition generation method. We will publish
source codes as soon as possible.

本文介绍了一种使用基于词义的外部词汇知识库的无监督视觉词义消歧方法，该方法提出了一种利用贝叶斯推断来合并词义信息的方法，以及一种与 GPT-3 一起使用的上下文感知定义生成方法，实验结果显示，我们的方法显著提高了 VWSD 性能，并且对于具有更好性能的现有定义生成方法的 OOD 样例实现的明显性能提升。