A retrieval-augmented prompting network for hateful meme detection - Kuang, Qiuhua; Lin, Yihao; Liu, Junxi; Lai, Xiazhi; Zhong, Runlu (Frontiers in Physics, 2025)
The rise of user-generated content on social media is making memes a prevalent medium for expression. However, some memes convey offensive information toward individuals or groups on particular aspects. Detecting such harmful content is essential to mitigate potential conflicts and harm. This paper proposes a retrieval-augmented prompting network (RAPN) for hateful meme detection. The proposed model utilizes a retrieval-augmented selector to identify semantically relevant prompting examples from diverse sources, enhancing the selection to better match the inference instances. Based on the prompting framework, attention networks are employed to extract critical features from input instance and examples. By applying contrastive learning to label and feature spaces, the model is capable of learning more discriminative information for classification. Comprehensive evaluations on benchmark datasets demonstrate that our model outperforms the baseline methods. Thereby, the proposed model has strong evidence of high accuracy on the task of hateful meme classification.