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

AbstractThis paper describes our zero-shot approachesfor the Visual Word Sense Disambiguation(VWSD) Task in English. Our preliminarystudy shows that the simple approach of match-ing candidate images with the phrase usingCLIP suffers from the many-to-many natureof image-text pairs. We find that the CLIP textencoder may have limited abilities in captur-ing the compositionality in natural language. Conversely, the descriptive focus of the phrasevaries from instance to instance. We addressthese issues in our two systems, Augment-CLIPand Stable Diffusion Sampling (SD Sampling).Augment-CLIP augments the text prompt bygenerating sentences that contain the contextphrase with the help of large language mod-els (LLMs). We further explore CLIP modelsin other languages, as the an ambiguous wordmay be translated into an unambiguous one inthe other language. SD Sampling uses text-to-image Stable Diffusion to generate multipleimages from the given phrase, increasing thelikelihood that a subset of images match theone that paired with the text.