D-HUMOR：通过多模态开放式推理理解黑色幽默
D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning
September 8, 2025
作者: Sai Kartheek Reddy Kasu, Mohammad Zia Ur Rehman, Shahid Shafi Dar, Rishi Bharat Junghare, Dhanvin Sanjay Namboodiri, Nagendra Kumar
cs.AI
摘要
网络迷因中的黑色幽默因其依赖隐含、敏感且具文化背景的线索而带来独特挑战。针对多模态内容中黑色幽默检测资源与方法的匮乏，我们引入了一个新颖的数据集，包含4,379个Reddit迷因，标注了黑色幽默、目标类别（性别、心理健康、暴力、种族、残疾及其他）以及三级强度评分（轻度、中度、重度）。基于此资源，我们提出了一种推理增强框架，该框架首先利用大型视觉-语言模型（VLM）为每个迷因生成结构化解释。通过角色反转自循环机制，VLM从作者视角出发，迭代优化其解释，确保完整性与一致性。随后，我们通过文本编码器从OCR转录文本及自我优化的推理中提取文本特征，同时使用视觉变换器获取视觉特征。三流交叉推理网络（TCRNet）通过成对注意力机制融合文本、图像及推理这三股信息流，生成统一表示用于分类。实验结果表明，我们的方法在黑色幽默检测、目标识别及强度预测三项任务上均优于强基线。数据集、标注及代码已公开，以促进多模态幽默理解与内容审核领域的进一步研究。代码与数据集获取地址：
https://github.com/Sai-Kartheek-Reddy/D-Humor-Dark-Humor-Understanding-via-Multimodal-Open-ended-Reasoning
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