MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection

Key points

• With the rapid proliferation of social media, harmful memes have become a growing threat to online ecosystems.1
• Recent studies have introduced Retrieval-Augmented Generation (RAG) and external knowledge into harmful meme detection.1
• By combining structured knowledge retrieval with an explicit multi-stage reasoning process inspired by cognitive mechanisms, MemeLogos leverages retrieved knowledge to guide harmfulness reasoning, improving both detection reliability and interpretability.1
• Extensive experiments show that MemeLogos consistently outperforms existing harmful meme detection methods while generating more coherent and interpretable reasoning, demonstrating the effectiveness of structured harmfulness knowledge for multimodal content understanding.1

Sources (1)

• [1]MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 09:02 AM

  To address these challenges, we propose MemeLogos, a knowledge-driven retrieval-augmented reasoning framework for harmful meme detection.

  With the rapid proliferation of social media, harmful memes have become a growing threat to online ecosystems.

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