@inproceedings{ghahroodi-etal-2023-sut,
title = "{SUT} at {S}em{E}val-2023 Task 1: Prompt Generation for Visual Word Sense Disambiguation",
author = "Ghahroodi, Omid and
Dalili, Seyed Arshan and
Mesforoush, Sahel and
Asgari, Ehsaneddin",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.298",
doi = "10.18653/v1/2023.semeval-1.298",
pages = "2160--2163",
abstract = "Visual 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 and words may have multiple senses. A proper V-WSD system can benefit applications like image retrieval and captioning. This paper proposes a Prompt Generation approach to solve this challenge. This approach improves the robustness of language-image models like CLIP to contextual ambiguities and helps them better correlate between textual and visual contexts of different senses of words.",
}
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<abstract>Visual 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 and words may have multiple senses. A proper V-WSD system can benefit applications like image retrieval and captioning. This paper proposes a Prompt Generation approach to solve this challenge. This approach improves the robustness of language-image models like CLIP to contextual ambiguities and helps them better correlate between textual and visual contexts of different senses of words.</abstract>
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%0 Conference Proceedings
%T SUT at SemEval-2023 Task 1: Prompt Generation for Visual Word Sense Disambiguation
%A Ghahroodi, Omid
%A Dalili, Seyed Arshan
%A Mesforoush, Sahel
%A Asgari, Ehsaneddin
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ghahroodi-etal-2023-sut
%X Visual 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 and words may have multiple senses. A proper V-WSD system can benefit applications like image retrieval and captioning. This paper proposes a Prompt Generation approach to solve this challenge. This approach improves the robustness of language-image models like CLIP to contextual ambiguities and helps them better correlate between textual and visual contexts of different senses of words.
%R 10.18653/v1/2023.semeval-1.298
%U https://aclanthology.org/2023.semeval-1.298
%U https://doi.org/10.18653/v1/2023.semeval-1.298
%P 2160-2163
Markdown (Informal)
[SUT at SemEval-2023 Task 1: Prompt Generation for Visual Word Sense Disambiguation](https://aclanthology.org/2023.semeval-1.298) (Ghahroodi et al., SemEval 2023)
ACL