SyRHM：基于符号语言增强与联想检索的零样本有害模因检测
SyRHM: Symbolic-Language-Enhanced Reasoning with Associative Retrieval for Zero-shot Harmful Meme Detection
- University of California, San Diego（加利福尼亚大学圣迭戈分校）
- Xi’an Jiaotong-Liverpool University（西交利物浦大学）
- University of Washington（华盛顿大学）
- The Australian National University（澳大利亚国立大学）
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
SyRHM通过联想检索和符号语言增强的多阶段推理，实现零样本有害模因检测，在FHM、HarM和MultiOff上表现优越。
AI中文摘要：
检测有害模因对于维护安全的在线社区至关重要。然而，有害意图往往是隐性的，源于视觉-文本不协调和文化刻板印象，这对现有的多模态检测器构成了挑战。我们提出了SyRHM，一个将有害模因检测分解为基于意义的检索和符号语言增强的多阶段推理的框架。SyRHM通过将多模态内容解析为文本元素和描述来检索语义相关的模因，提供超越表面相似性的有根据的上下文。基于检索到的上下文，SyRHM使用翻译器阶段将多模态输入转换为符号中间表示，然后通过规划器和求解器阶段执行多阶段推理，实现对有害意图的表达性和可解释性分析。在FHM、HarM和MultiOff上的实验证明了SyRHM的有效性，在大多数评估设置中优于多模态和基于推理的基线，同时为有害内容提供推理轨迹。代码可在以下网址获取：this https URL
英文摘要：
Detecting harmful memes is critical for maintaining safe online communities. However, harmful intent is often implicit, arising from visual-textual incongruity and cultural stereotypes, which challenges existing multimodal detectors. We propose SyRHM, a framework that decomposes harmful meme detection into meaning-grounded retrieval and symbolic-language-enhanced multi-stage reasoning. SyRHM retrieves semantically related memes by parsing multimodal content into textual elements and descriptions, providing grounded context beyond surface-level similarity. Building on the retrieved context, SyRHM uses a translator stage to convert multimodal inputs into symbolic intermediate representations, and then performs multi-stage reasoning via planner and solver stages, enabling expressive and interpretable analysis of harmful intent. Experiments on FHM, HarM, and MultiOff demonstrate the effectiveness of SyRHM, achieving superior performance on most evaluation settings against multimodal and reasoning-based baselines, while providing reasoning traces for harmful content. The code is available at: https://github.com/Scabbards1500/SyRHM
