arXiv cs.CL· Jiahao Qin, Yiwen Wang·· 10 小时前AI 评分20
MoIF：面向多模态幽默检测的阶次选择性谱正则化交互过滤
M\"obius Interaction Filtering: Order-Selective Spectral Regularization for Multimodal Humor Detection
AI 导读
研究者提出 Möbius 交互过滤（MoIF），通过模态子集预测函数与聚合结构实现阶次选择性谱正则化，在保留高阶交互表示的同时调控其对预测的贡献。在 UR-FUNNY 上五种子实验取得 70.6% 准确率与加权 F1，超过 13 种复现方法，扰动下标签对齐信噪比较联合训练三模态预测器提升 1.62-2.79 倍。
正文 · 原文
Abstract:Multimodal humor detection requires interpreting language, vocal delivery, and visual behavior jointly. Expressive fusion must also regulate how interactions of different orders enter the final judgment. We propose Möbius Interaction Filtering (MoIF), which learns a modality-subset prediction function and imposes order-selective spectral regularization through its aggregation structure. Private temporal encoders and balanced reciprocal interaction construct supervised unimodal, bimodal, and trimodal predictors. Their uniform aggregate is exactly a Möbius-domain filter with a geometrically decreasing interaction-order response. This formulation retains higher-order representations while regulating their contribution to prediction. Controlled UR-FUNNY perturbations reveal a pronounced decline in label-aligned signal-to-noise ratio across interaction orders. The induced filter improves the final ratio by 1.62-2.79 times relative to the co-trained trimodal predictor while preserving its own clean class-aligned signal under perturbation. Five-seed experiments achieve 70.6% accuracy and weighted F1 on UR-FUNNY, exceeding thirteen reproduced methods on these detection metrics. CMU-MOSI and CMU-MOSEI evaluations establish applicability to sentiment prediction, and cross-seed analyses show more stable estimates on all three datasets. MoIF connects the organization of multimodal interactions to their statistical reliability through an executable spectral constraint. Code is available at this https URL.
| Comments: | 21 pages, 5 figures, 6 tables |
| Subjects: | Multimedia (cs.MM); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.12913 [cs.MM] |
| (or arXiv:2611.12913v1 [cs.MM] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12913 arXiv-issued DOI via DataCite |
Submission history
From: Jiahao Qin [view email]
[v1]
Sun, 25 Oct 2026 19:55:54 UTC (952 KB)
来源：arXiv cs.CL · arxiv.org