IIITH at SemEval-2022 Task 5: A comparative study of deep learning models for identifying misogynous memes
Tathagata Raha, Sagar Joshi, Vasudeva Varma · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
This paper provides a comparison of different deep learning methods for identifying misogynous memes for SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification.In this task, we experiment with architectures in the identification of misogynous content in memes by making use of text and image-based information.The different deep learning methods compared in this paper are: (i) unimodal image or text models (ii) fusion of unimodal models (iii) multimodal transformers models and (iv) transformers further pretrained on a multimodal task.From our experiments, we found pretrained multimodal transformer architectures to strongly outperform the models involving fusion of representation from both the modalities.