VISAFF: Speaker-Centered Visual Affective Feature Learning for Emotion Recognition in Conversation

• URL: http://arxiv.org/abs/2605.18547v1
• Date: Mon, 18 May 2026 15:27:10 GMT
• Title: VISAFF: Speaker-Centered Visual Affective Feature Learning for Emotion Recognition in Conversation
• Abstract summary: Emotion Recognition in Conversation (ERC) is essential for effective human-machine interaction.<n>Recent Vision-Language Models (VLMs) are not inherently tailored for ERC.<n>We propose VISAFF, a speaker-centered VISual AFFective feature learning framework for ERC.
• Score: 17.099995082943735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion Recognition in Conversation (ERC) is essential for effective human-machine interaction, aiming to identify speakers' emotional states in multi-turn dialogues. Early text-based methods struggle with complex scenarios like sarcasm because they inherently neglect vital non-verbal information. While recent Vision-Language Models (VLMs) address this by analyzing video directly, they are not inherently tailored for ERC and often focus on emotionally irrelevant background regions or passive listeners rather than the active speaker. Furthermore, fine-tuning these large models incurs prohibitive computational costs. Additionally, isolated visual signals are frequently ambiguous or technically compromised without the context of linguistic content and vocal prosody. To address these challenges, we propose VISAFF, a speaker-centered VISual AFFective feature learning framework for ERC. VISAFF consists of two stages: Speaker-Centered Affective Grounding and Reliability-Guided Affective Complementation. VISAFF utilizes a tuning-free approach to unlock the reasoning capabilities of frozen VLMs, efficiently steering them to focus on the active speaker's emotional visual cues without heavy training overheads. In the second stage, we introduce a reliability-guided affective complementation mechanism that dynamically leverages textual and acoustic modalities to compensate for visual uncertainty. Experiments on two real-world datasets demonstrate that VISAFF achieves highly competitive performance compared to state-of-the-art methods in a tuning-free setting, significantly enhancing computational efficiency by eliminating the need for expensive fine-tuning of large VLMs. The source code is available at https://anonymous.4open.science/r/speaker-2365/.

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