无需标注数据，用多智能体协作识别新型有害表情包。
MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
- 从无标注图库中找相似表情包提供上下文。
- 双向推理机制全面理解相似内容特征。
- 多智能体辩论确保判断可靠，适合新类型内容。
社交媒体上表情包的快速传播凸显了检测有害内容的迫切需求。然而，传统数据驱动方法因内容持续演化和缺乏实时标注数据，难以应对新型表情包。为此，我们提出MIND——一种无需标注数据的零样本有害表情包检测多智能体框架。该框架包含三项核心策略：1）从无标注参考集检索相似表情包以提供上下文信息；2）提出双向洞察推导机制，全面理解相似内容；3）采用多智能体辩论机制，通过理性仲裁实现稳健决策。在三个表情包数据集上的大量实验表明，该框架不仅优于现有零样本方法，且在不同模型架构与参数规模下均表现出强泛化能力，为有害表情包检测提供了可扩展解决方案。代码已公开于 https://github.com/destroy-lonely/MIND。
原文摘要 · Abstract (English)
The rapid expansion of memes on social media has highlighted the urgent need for effective approaches to detect harmful content. However, traditional data-driven approaches struggle to detect new memes due to their evolving nature and the lack of up-to-date annotated data. To address this issue, we propose MIND, a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. MIND implements three key strategies: 1) We retrieve similar memes from an unannotated reference set to provide contextual information. 2) We propose a bi-directional insight derivation mechanism to extract a comprehensive understanding of similar memes. 3) We then employ a multi-agent debate mechanism to ensure robust decision-making through reasoned arbitration. Extensive experiments on three meme datasets demonstrate that our proposed framework not only outperforms existing zero-shot approaches but also shows strong generalization across different model architectures and parameter scales, providing a scalable solution for harmful meme detection. The code is available at https://github.com/destroy-lonely/MIND.
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