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Video2Reaction dataset maps video to audience emotions · 2 sources tracked

Researchers have introduced Video2Reaction, a new multimodal dataset and benchmark designed to predict audience emotional responses to video content. The dataset, comprising over 10,000 videos, utilizes a two-stage pipeline with open-source LLMs to annotate induced emotions from social media, achieving 86% correctness. While finetuned foundation video models show promise, even state-of-the-art methods like LLaVA-NeXT struggle with the inherent subjectivity, achieving only 77% Top-3 F1 in dominant reaction prediction. AI

IMPACT This dataset could advance research in video understanding and personalized content recommendation by enabling better prediction of audience emotional responses.

RANK_REASON The cluster describes a new academic paper introducing a dataset and benchmark for a specific AI task.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Video2Reaction dataset maps video to audience emotions · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Trang Nguyen, Sidong Zhang, Shiv Shankar, Gauri Jagatap, Deepak Chandran, Andrea Fanelli, Madalina Fiterau ·

    Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild

    arXiv:2607.06875v1 Announce Type: cross Abstract: Understanding and forecasting audience reactions to video content are crucial for improving content creation, recommendation systems, and media analysis. To enable audience reaction prediction and other content engagement applicat…

  2. arXiv cs.CV TIER_1 English(EN) · Madalina Fiterau ·

    Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild

    Understanding and forecasting audience reactions to video content are crucial for improving content creation, recommendation systems, and media analysis. To enable audience reaction prediction and other content engagement applications, we introduce $\textbf{Video2Reaction}$, a mu…