arXiv:2608.23172v1 Announce Type: new
Abstract: Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humo...
By Abhilash Nandy, Rahul Seetharaman, Aman Bansal, Rounak Saha, Manav Nitin Kapadnis, Millon Madhur Das, Pawan Goyal, Niloy Ganguly
arXiv:2509. 25773v3 Announce Type: replace-cross Abstract: AI models capable of comprehending humor hold real-world promise -- for example, enhancing engagement in human-machine interactions.
By Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, Yuxuan Wang, Qinrong Cui, Wei Bi, Songchun Zhu, Bo Zhao, Zilong Zheng
The paper introduces IRS (Incongruity-Resolution Supervision), a framework that breaks humor understanding into three parts: identifying mismatches in a visual scene, creating coherent reinterpretations of those mismatches, and aligning these interpretations with human preferences. IRS uses structured reasoning traces to guide models from visual perception to humorous interpretation, and it is evaluated on the New Yorker Cartoon Caption Contest. Experiments on 7B, 32B, and 72B models show that IRS improves caption matching and ranking, with the 72B model achieving 76.10% ranking accuracy—outperforming non-expert humans and all other multimodal baselines—and demonstrates transferable reasoning patterns in zero‑shot settings.
By Hatice Merve Vural, Doga Kukul, Ege Erdem Ozlu, Demir Ekin Arikan, Bob Mankoff, Erkut Erdem, Aykut Erdem
MultiHuSE is a multimodal dataset featuring 2,407 high‑definition videos of 50 diverse actors delivering 1,463 text samples in four psychological humour styles—affiliative, aggressive, self‑enhancing, and self‑deprecating—plus neutral content. Each text is performed by multiple actors, allowing analysis of expressive diversity, and a subset includes emotion annotations. Baseline experiments show that multimodal fusion improves humour style classification accuracy over unimodal approaches, especially for affiliative humour.
By Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat
arXiv:2607. 15442v1 Announce Type: new Abstract: Internet memes intertwine visual cues, textual content, and cultural context, making them particularly challenging to interpret in scenarios where humor, sarcasm, and harmful intent coexist.
By Shanhong Liu, Pai Chet Ng, De Wen Soh, Malika Meghjani, Konstantinos N. Plataniotis
arXiv:2606. 00046v1 Announce Type: cross Abstract: Video platforms such as YouTube have reshaped how users engage with entertainment and information, emphasizing brief, highly engaging content such as Shorts.
By Sydney Johns, Sanjeev Parthasarathy, Shantnu Bhalla, Vaibhav Garg