@inproceedings{yang-etal-2023-tam,
    title = "{TAM} of {SCNU} at {S}em{E}val-2023 Task 1: {FCLL}: A Fine-grained Contrastive Language-Image Learning Model for Cross-language Visual Word Sense Disambiguation",
    author = "Yang, Qihao  and
      Li, Yong  and
      Wang, Xuelin  and
      Li, Shunhao  and
      Hao, Tianyong",
    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-1.70",
    doi = "10.18653/v1/2023.semeval-1.70",
    pages = "506--511",
    abstract = "Visual 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 contextual information and cross-linguistic background knowledge in text processing make this task challenging. To alleviate this issue, we propose a Fine-grained Contrastive Language-Image Learning (FCLL) model, which learns fine-grained image-text knowledge by employing a new fine-grained contrastive learning mechanism and enriches contextual information by establishing relationship between concepts and sentences. In addition, a new multimodal-multilingual knowledge base involving ambiguous target words is constructed for visual WSD. Experiment results on the benchmark datasets from SemEval-2023 Task 1 show that our FCLL ranks at the first in overall evaluation with an average H@1 of 72.56{\textbackslash}{\%} and an average MRR of 82.22{\textbackslash}{\%}. The results demonstrate that FCLL is effective in inference on fine-grained language-vision knowledge. Source codes and the knowledge base are publicly available at \url{https://github.com/CharlesYang030/FCLL}.",
}

Visual 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 contextual information and cross-linguistic background knowledge in text processing make this task challenging. To alleviate this issue, we propose a Fine-grained Contrastive Language-Image Learning (FCLL) model, which learns fine-grained image-text knowledge by employing a new fine-grained contrastive learning mechanism and enriches contextual information by establishing relationship between concepts and sentences. In addition, a new multimodal-multilingual knowledge base involving ambiguous target words is constructed for visual WSD. Experiment results on the benchmark datasets from SemEval-2023 Task 1 show that our FCLL ranks at the first in overall evaluation with an average H@1 of 72.56{\textbackslash}{\%} and an average MRR of 82.22{\textbackslash}{\%}. The results demonstrate that FCLL is effective in inference on fine-grained language-vision knowledge. Source codes and the knowledge base are publicly available at \url{https://github.com/CharlesYang030/FCLL}.
Yang, Qihao Li, Yong Wang, Xuelin Li, Shunhao Hao, Tianyong
TAM of SCNU at SemEval-2023 Task 1: FCLL: A Fine-grained Contrastive Language-Image Learning Model for Cross-language Visual Word Sense Disambiguation