Video2Reaction is a multimodal dataset that links short movie segments to the emotional reactions of viewers, gathered from social media comments. The dataset models reactions as distributions over categorical emotions, capturing the subjective and ambiguous nature of emotional perception. Experiments show that vision‑language models fine‑tuned with LoRA learn effectively from Video2Reaction and outperform specialized baselines, and that models pre‑fine‑tuned on this dataset transfer well to other emotion prediction tasks.
By Sidong Zhang, Trang Nguyen, Shiv Shankar, Gauri Jagatap, Deepak Chandran, Andrea Fanelli, Madalina Fiterau
arXiv:2610.00809v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) enable video annotation at scale, but costs accumulate quickly: processing a typical 60-second short-form video at one fr...
By Zhixi Zhu, Kristina Gligoric
M3TR is a temporal retrieval‑enhanced multi‑modal framework for predicting micro‑video popularity. It introduces a Mamba‑Hawkes Process module to model user feedback as self‑exciting events, capturing long‑range temporal dependencies. A temporal‑aware retrieval engine then identifies historically relevant videos by combining multi‑modal content similarity with popularity trajectory similarity, augmenting the target video’s features for improved prediction accuracy.
By Jiacheng Lu, Weijian Wang, Mingyuan Xiao, Yang Hua, Tao Song, Bo Peng, Cheng Hua, Haibing Guan
arXiv:2607. 02680v1 Announce Type: cross Abstract: MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text.
By Khush Attarde, Yusuf Ali, Megha Thukral, Divye Bhutani, Thomas Ploetz, Zsolt Kira
arXiv:2606. 19627v1 Announce Type: cross Abstract: The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds.
By Katya Mirylenka, Egor Malykh, Mahdyar Ravanbakhsh, Michael Gygli, Marco-Andrea Buchmann, Andrew Dzhoha, Svitlana Borzenko, Francesca Catino, Mohamed Gaafar, Maarten Versteegh, Thomas Kober, Dario d'Andrea, Ellie Langhans
arXiv:2606. 15694v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in understanding complex multimodal content.
By Hangling Xie