  Women are influential online, especially in image-based social media such as
Twitter and Instagram. However, many in the network environment contain gender
discrimination and aggressive information, which magnify gender stereotypes and
gender inequality. Therefore, the filtering of illegal content such as gender
discrimination is essential to maintain a healthy social network environment.
In this paper, we describe the system developed by our team for SemEval-2022
Task 5: Multimedia Automatic Misogyny Identification. More specifically, we
introduce two novel system to analyze these posts: a multimodal multi-task
learning architecture that combines Bertweet for text encoding with ResNet-18
for image representation, and a single-flow transformer structure which
combines text embeddings from BERT-Embeddings and image embeddings from several
different modules such as EfficientNet and ResNet. In this manner, we show that
the information behind them can be properly revealed. Our approach achieves
good performance on each of the two subtasks of the current competition,
ranking 15th for Subtask A (0.746 macro F1-score), 11th for Subtask B (0.706
macro F1-score) while exceeding the official baseline results by high margins.
