构建首个暗黑幽默多模态数据集，提升网络迷因内容理解与审核能力。
D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning -- A Benchmark Dataset and Method
- 用大模型生成结构化解释，通过角色互换自循环优化推理过程。
- 在4379张迷因上实现暗黑幽默检测、目标类别与强度预测的最优性能。
- 适合研究多模态内容理解、敏感信息识别及智能审核的学者与工程师。
在线迷因中的暗黑幽默因其依赖隐含、敏感且文化相关的线索而带来独特挑战。为解决多模态内容中暗黑幽默检测资源与方法匮乏的问题，我们构建了一个包含4,379条Reddit迷因的新数据集，均标注了暗黑幽默属性、目标类别（性别、心理健康、暴力、种族、残障及其他）以及三级强度评分（轻度、中度、重度）。基于此资源，我们提出一种增强推理的框架：首先利用大视觉语言模型（VLM）生成每张迷因的结构化解释；通过角色反转自循环机制，VLM以作者视角迭代优化解释，确保完整性和一致性；随后从OCR文本与自优化推理中提取文本特征，结合视觉变换器获取图像特征；最后通过三流交叉推理网络（TCRNet），以成对注意力融合文本、图像与推理三路信息，生成统一表征用于分类。实验表明，该方法在暗黑幽默检测、目标识别和强度预测三项任务中均超越强基线。数据集、标注与代码已开源，以促进多模态幽默理解与内容审核研究。代码与数据集见：https://github.com/Sai-Kartheek-Reddy/D-Humor-Dark-Humor-Understanding-via-Multimodal-Open-ended-Reasoning
原文摘要 · Abstract (English)
Dark humor in online memes poses unique challenges due to its reliance on implicit, sensitive, and culturally contextual cues. To address the lack of resources and methods for detecting dark humor in multimodal content, we introduce a novel dataset of 4,379 Reddit memes annotated for dark humor, target category (gender, mental health, violence, race, disability, and other), and a three-level intensity rating (mild, moderate, severe). Building on this resource, we propose a reasoning-augmented framework that first generates structured explanations for each meme using a Large Vision-Language Model (VLM). Through a Role-Reversal Self-Loop, VLM adopts the author's perspective to iteratively refine its explanations, ensuring completeness and alignment. We then extract textual features from both the OCR transcript and the self-refined reasoning via a text encoder, while visual features are obtained using a vision transformer. A Tri-stream Cross-Reasoning Network (TCRNet) fuses these three streams, text, image, and reasoning, via pairwise attention mechanisms, producing a unified representation for classification. Experimental results demonstrate that our approach outperforms strong baselines across three tasks: dark humor detection, target identification, and intensity prediction. The dataset, annotations, and code are released to facilitate further research in multimodal humor understanding and content moderation. Code and Dataset are available at: https://github.com/Sai-Kartheek-Reddy/D-Humor-Dark-Humor-Understanding-via-Multimodal-Open-ended-Reasoning
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