aiqbench analysis
This arXiv preprint proposes CLEAR, a method for emotion recognition in multimodal conversations. It uses the internal representations of an emotion-supervised vision-language model rather than its generated labels, and adds residual coupling to preserve local text and audio cues. The reported results are on IEMOCAP and MELD and are described only as "competitive," so readers should check the benchmark numbers against existing baselines before drawing conclusions.
CLEAR: Coupling Local Evidence with Affective Representations for Multimodal Emotion Recognition in Conversation
arXiv:2605.18547v2 Announce Type: replace Abstract: In video calls and livestreamed discussions, recognizing speakers' emotions helps identify emotionally salient segments and supports fine-grained video content analysis. Multimodal Emotion Recognition in Conversation (ERC) provides an approach to recognizing emotions in such content by integrating textual, visual, and acoustic information with conversational context. Current mainstream emotion recognition methods largely leverage the contextual understanding capabilities of multimodal large language models (MLLMs) to predict emotions by generating labels. However, emotion understanding and label prediction may be inconsistent, leaving the internal affective information underutilized. Moreover, when the current utterance is jointly modeled with extensive dialogue history and multimodal information, its local discriminative cues may be diluted and become less salient in the resulting contextual representation. To address these limitations, we propose CLEAR, an ERC framework that couples multimodal contextual representations with local discriminative cues. To mitigate the mismatch between emotion understanding and label prediction, we use the continuous internal representation of an emotion-supervised vision-language model (VLM) instead of its generated emotion labels, and further train a classifier based on this representation. To strengthen local discriminative cues, we use sample-adaptive residual coupling to dynamically integrate textual and acoustic evidence from the target utterance into the contextual representation. Experiments on IEMOCAP and MELD demonstrate that CLEAR achieves competitive performance, supporting the effectiveness of the proposed approach. The code is available at https://anonymous.4open.science/r/vlm-210F/.
Abstract:In video calls and livestreamed discussions, recognizing speakers' emotions helps identify emotionally salient segments and supports fine-grained video content analysis. Multimodal Emotion Recognition in Conversation (ERC) provides an approach to recognizing emotions in such content by integrating textual, visual, and acoustic information with conversational context. Current mainstream emotion recognition methods largely leverage the contextual understanding capabilities of multimodal large language models (MLLMs) to predict emotions by generating labels. However, emotion understanding and label prediction may be inconsistent, leaving the internal affective information underutilized. Moreover, when the current utterance is jointly modeled with extensive dialogue history and multimodal information, its local discriminative cues may be diluted and become less salient in the resulting contextual representation. To address these limitations, we propose CLEAR, an ERC framework that couples multimodal contextual representations with local discriminative cues. To mitigate the mismatch between emotion understanding and label prediction, we use the continuous internal representation of an emotion-supervised vision-language model (VLM) instead of its generated emotion labels, and further train a classifier based on this representation. To strengthen local discriminative cues, we use sample-adaptive residual coupling to dynamically integrate textual and acoustic evidence from the target utterance into the contextual representation. Experiments on IEMOCAP and MELD demonstrate that CLEAR achieves competitive performance, supporting the effectiveness of the proposed approach. The code is available at this https URL.
Artificial Intelligence (cs.AI)
arXiv:2605.18547 [cs.AI]
(or arXiv:2605.18547v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.18547 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 313 words
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