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New dataset trains AI to predict audience emotional reactions from videos

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Video2Reaction, a new dataset designed to train video foundation models to predict audience emotional responses. This dataset maps short video clips to viewer reactions expressed through social media comments, modeling emotions as distributions to capture subjectivity. Benchmarking finetuned vision-language models (VLMs) like LLaVA-NeXT-Video-7B, the study shows that VLMs trained on Video2Reaction can effectively predict dominant reactions and transfer this capability to other emotion-related datasets, achieving performance comparable to models trained on full datasets. AI

IMPACT This dataset could enable AI systems to better understand and respond to human emotional cues in video content.

RANK_REASON The cluster describes a new academic paper introducing a dataset and benchmarking models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset trains AI to predict audience emotional reactions from videos

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The cluster describes a new academic paper introducing a dataset and benchmarking models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    Video2Reaction: Training Foundation Video Models to Predict Audience Reaction

    arXiv:2609.01816v1 Announce Type: new Abstract: We introduce Video2Reaction, a multimodal dataset that maps short movie segments to the induced emotional reactions of viewers in the wild, as expressed through social media comments. Video2Reaction captures the natural diversity of…

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