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This paper describes our zero-shot approaches for the Visual Word SenseDisambiguation (VWSD) Task in English. Our preliminary study shows that thesimple approach of matching candidate images with the phrase using CLIP suffersfrom the many-to-many nature of image-text pairs. We find that the CLIP textencoder may have limited abilities in capturing the compositionality in naturallanguage. Conversely, the descriptive focus of the phrase varies from instanceto instance. We address these issues in our two systems, Augment-CLIP andStable Diffusion Sampling (SD Sampling). Augment-CLIP augments the text promptby generating sentences that contain the context phrase with the help of largelanguage models (LLMs). We further explore CLIP models in other languages, asthe an ambiguous word may be translated into an unambiguous one in the otherlanguage. SD Sampling uses text-to-image Stable Diffusion to generate multipleimages from the given phrase, increasing the likelihood that a subset of imagesmatch the one that paired with the text.

Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling  Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt  Augmentation and Text-To-Image Diffusion