D-HUMOR：基於多模態開放式推理的黑色幽默理解
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
網絡迷因中的黑色幽默因其依賴於隱含、敏感且具有文化背景的線索而帶來獨特挑戰。為解決多模態內容中黑色幽默檢測資源與方法的不足，我們引入了一個包含4,379個Reddit迷因的新數據集，這些迷因已針對黑色幽默、目標類別（性別、心理健康、暴力、種族、殘疾及其他）以及三級強度評分（輕微、中等、嚴重）進行了標註。基於此資源，我們提出了一種推理增強框架，該框架首先利用大型視覺-語言模型（VLM）為每個迷因生成結構化解釋。通過角色反轉自循環，VLM從作者視角出發，迭代精煉其解釋，確保完整性和一致性。隨後，我們從OCR轉錄文本及自我精煉的推理中提取文本特徵，同時使用視覺變換器獲取視覺特徵。三流交叉推理網絡（TCRNet）通過成對注意力機制融合這三個流——文本、圖像和推理，生成用於分類的統一表示。實驗結果表明，我們的方法在三個任務上均優於強基線：黑色幽默檢測、目標識別及強度預測。數據集、註釋及代碼已公開，以促進多模態幽默理解與內容審核的進一步研究。代碼與數據集可訪問：
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