Emotion recognition from video involves many nuanced challenges. Models that depend exclusively on either visual or audio signals often miss the intricate interplay between these modalities, leading to misinterpretations of emotional content. A key difficulty is reliably combining visual cues—such as facial expressions or body language—with auditory signals like tone or intonation. Many existing systems also lack the capability to explain their decision-making process, which makes it hard to understand how a specific emotion is detected. Furthermore, these models can sometimes generate reasoning that does not directly reflect the input data, or they might fail to fully utilize important audio details. These issues become even more pronounced when models encounter unfamiliar scenarios, emphasizing the need for a more robust and interpretable approach to multimodal emotion recognition. Introducing R1-Omni by […]
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