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New dataset Chehre probes video models' perception of facial expressions

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Chehre, a new dataset designed to explore perceptual variations in how video language models interpret facial expressions. The dataset comprises over 2,000 videos, with each video annotated by approximately 30 individuals, capturing a wide range of dynamic facial expressions prompted by emojis. This resource enables a new task called "distributional expression recognition," which evaluates a model's ability to replicate the diversity of human perception. Initial tests indicate that persona prompting can effectively guide model perception and better align it with human annotator responses. AI

IMPACT This dataset could lead to more robust video language models capable of understanding nuanced human expressions.

RANK_REASON The cluster describes a new dataset and research paper published on arXiv, focusing on a novel task for evaluating video language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

New dataset Chehre probes video models' perception of facial expressions

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The cluster describes a new dataset and research paper published on arXiv, focusing on a novel task for evaluating video language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Bita Azari, Zoe Stanley, Avneet Batra, Poorvi Bhatia, Hali Kil, Manolis Savva, Angelica Lim ·

    Chehre: An Emoji-Prompted Dataset to Explore Perceptual Flexibility in Video Language Models

    arXiv:2606.21657v2 Announce Type: replace-cross Abstract: Do people perceive the same facial expression in the same way? Should we expect vision models to be flexible in how they perceive facial expressions? Facial expressions are nonverbal social signals used in human interactio…

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