In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-speech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions. To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN). Additionally, we explore an ensemble using the aforementioned models. Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Sub-task B positioning our team in the upper third of the leaderboard. We release the code and experiments for our models on GitHub

本文描述了我们提交到SemEval-2022 Task 5的分类系统，旨在通过分析文本和meme图像一起识别多方面的厌恶内容，我们提出了两种基于预训练UNITER模型的模型和一种基于词表图卷网络的模型，并探索了一个集合方法，我们最好的模型在Sub-task A达到71.4%的F1值，在Sub-task B达到67.3％的F1值，这将我们的团队排在排行榜的前三分之一。我们在GitHub上发布了我们的代码和实验。

UPB在SemEval-2022任务5中：利用图像情感和图卷积网络增强UNITER，用于多媒体自动骚扰辨识