@inproceedings{sharma-etal-2022-r2d2,
    title = "{R}2{D}2 at {S}em{E}val-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes",
    author = "Sharma, Mayukh  and
      Kandasamy, Ilanthenral  and
      W B, Vasantha",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.semeval-1.106/",
    doi = "10.18653/v1/2022.semeval-1.106",
    pages = "761--770",
    abstract = "This paper describes the multimodal deep learning system proposed for SemEval 2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. We participated in both Subtasks, i.e. Subtask A: Misogynous meme identification, and Subtask B: Identifying type of misogyny among potential overlapping categories (stereotype, shaming, objectification, violence). The proposed architecture uses pre-trained models as feature extractors for text and images. We use these features to learn multimodal representation using methods like concatenation and scaled dot product attention. Classification layers are used on fused features as per the subtask definition. We also performed experiments using unimodal models for setting up comparative baselines. Our best performing system achieved an F1 score of 0.757 and was ranked $3^{rd}$ in Subtask A. On Subtask B, our system performed well with an F1 score of 0.690 and was ranked $10^{th}$ on the leaderboard. We further show extensive experiments using combinations of different pre-trained models which will be helpful as baselines for future work."
}