Visual Word Sense Disambiguation

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 …...

AbstractThis paper presents an approach to tackle the task of Visual Word Sense Disambiguation (Visual-WSD), which involves determining the most appropriate image to represent a given polysemous word in one …...

AbstractOur team focuses on the multimodal domain of images and texts, we propose a model that can learn the matching relationship between text-image pairs by contrastive learning. More specifically, We …...

AbstractGiven a word in context, the task of VisualWord Sense Disambiguation consists of select-ing the correct image among a set of candidates. To select the correct image, we propose a …...

AbstractThe objective of the SemEval-2023 Task 1: Visual Word Sense Disambiguation (VWSD) is to identify the image illustrating the indented meaning of a target word and some minimal additional context. …...

AbstractThe goal of visual word sense disambiguation is to find the image that best matches the provided description of the word’s meaning. It is a challenging problem, requiring approaches that …...

AbstractThis article presents our solution for SemEval-2023 Task 1: Visual Word Sense Disambiguation. The aim of the task was to select the most suitable from a list of ten images …...

AbstractVisual Word Sense Disambiguation (VWSD) task aims to find the most related image among 10 images to an ambiguous word in some limited textual context. In this work, we use …...

AbstractVisual-Word Sense Disambiguation (V-WSD) entails resolving the linguistic ambiguity in a text by selecting a clarifying image from a set of (potentially misleading) candidates. In this paper, we address V-WSD …...

AbstractThis paper describes the participation of the RCLN team at the Visual Word Sense Disambiguation task at SemEval 2023. The participation was focused on the use of CLIP as a …...

AbstractThis paper describes our system used in SemEval-2023 Task-1: Visual Word Sense Disambiguation (VWSD). The VWSD task is to identify the correct image that corresponds to an ambiguous target word …...

AbstractBased on recent progress in image-text retrieval techniques, this paper presents a fine-tuned model for the Visual Word Sense Disambiguation (VWSD) task. The proposed system fine-tunes a pre-trained model using …...

AbstractThe Visual Word Sense Disambiguation (VWSD) shared task aims at selecting the image among candidates that best interprets the semantics of a target word with a short-length phrase for English, …...

AbstractVisual Word Sense Disambiguation (V-WSD) identifies the correct visual sense of a multi-sense word in a specific context. This can be challenging as images may need to provide additional context …...

AbstractIn this paper, we introduce our submission in the task of visual word sense disambiguation (vWSD). Our proposed solution operates by deriving quasi-symbolic semantic categories from the hidden representations of …...

AbstractVisual Word Sense Disambiguation (WSD), as a fine-grained image-text retrieval task, aims to identify the images that are relevant to ambiguous target words or phrases. However, the difficulties of limited …...

AbstractVisual Word Sense Disambiguation shared task at SemEval-2023 aims to identify an image corresponding to the intended meaning of a given ambiguous word (with related context) from a set of …...

AbstractWe describe the systems of the University of Alberta team for the SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel algorithm that leverages glosses retrieved from BabelNet, …...

AbstractVisual 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 …...