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New RASG framework enhances emotion recognition using listener-only data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called RASG for recognizing interlocutor emotions, which focuses on predicting listener emotions using only listener videos and speaker audios. The framework incorporates Role-aware Visual Transfer (RVT) to address data scarcity for listeners and Stimulus-guided Boundary Reasoning (SBR) to disambiguate similar visual reactions based on speaker context. This approach achieved a 76.25% accuracy on the MER-Cross dataset and secured second place in the MER Grand Challenge at ACM MM 2026. AI

IMPACT This research advances the field of emotion recognition by enabling analysis with limited listener data, potentially impacting applications in human-computer interaction and social robotics.

RANK_REASON The cluster contains a research paper detailing a new framework for emotion recognition. [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 RASG framework enhances emotion recognition using listener-only data

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The cluster contains a research paper detailing a new framework for emotion recognition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Wei Wang, Zhaowu Li, Jianjie Luo, Fu Lee Wang, Lap-Kei Lee, Zhenguo Yang ·

    From Expression to Reaction: Role-aware Visual Transfer and Stimulus-guided Reasoning for Interlocutor Emotion Recognition

    arXiv:2610.03016v1 Announce Type: cross Abstract: In this paper, we propose a Role-aware Stimulus-guided (RASG) framework for interlocutor emotion recognition, which predicts listener emotions from listener-only videos and speaker-only audios. RASG consists of Role-aware Visual T…

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