Code used by Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework.

@inproceedings{mahran-etal-2022-codec,
    title = "Codec at {S}em{E}val-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework",
    author = "Mahran, Ahmed  and
      Alessandro Borella, Carlo  and
      Perifanos, Konstantinos",
    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://aclanthology.org/2022.semeval-1.93",
    doi = "10.18653/v1/2022.semeval-1.93",
    pages = "679--688",
    abstract = "In this paper we describe our work towards building a generic framework for both multi-modal embedding and multi-label binary classification tasks, while participating in task 5 (Multimedia Automatic Misogyny Identification) of SemEval 2022 competition.Since pretraining deep models from scratch is a resource and data hungry task, our approach is based on three main strategies. We combine different state-of-the-art architectures to capture a wide spectrum of semantic signals from the multi-modal input. We employ a multi-task learning scheme to be able to use multiple datasets from the same knowledge domain to help increase the model{'}s performance. We also use multiple objectives to regularize and fine tune different system components.",
}