Pro-Cap: Leveraging a frozen vision-language model for hateful meme detection

• Main Authors: CAO, Rui, HEE, Ming Shan, KUEK, Adriel, CHONG, Wen Haw, LEE, Roy Ka-Wei, JIANG, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Memes; multimodal; semantic extraction; Databases and Information Systems; Graphic Communications; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/8477; https://ink.library.smu.edu.sg/context/sis_research/article/9480/viewcontent/Pro_Cap_pvoa_cc_by.pdf
• Institution: Singapore Management University

Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from non-informative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot visual question answering (VQA) manner. Specifically, we prompt a frozen PVLM by asking hateful content-related questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection. The good performance of models with Pro-Cap on three benchmarks validates the effectiveness and generalization of the proposed method 1.