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Visual Word Sense Disambiguation (VWSD) is a task to find the image that mostaccurately depicts the correct sense of the target word for the given context.Previously, image-text matching models often suffered from recognizingpolysemous words. This paper introduces an unsupervised VWSD approach that usesgloss information of an external lexical knowledge-base, especially the sensedefinitions. Specifically, we suggest employing Bayesian inference toincorporate the sense definitions when sense information of the answer is notprovided. In addition, to ameliorate the out-of-dictionary (OOD) issue, wepropose a context-aware definition generation with GPT-3. Experimental resultsshow that the VWSD performance significantly increased with our Bayesianinference-based approach. In addition, our context-aware definition generationachieved prominent performance improvement in OOD examples exhibiting betterperformance than the existing definition generation method.

Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information