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arXiv Computer Vision By Nicolas Richet, Soufiane Belharbi, Haseeb Aslam, Meike Emilie Schadt, Manuela Gonz\'alez-Gonz\'alez, Gustave Cortal, Alessandro Lameiras Koerich, Marco Pedersoli, Alain Finkel, Simon Bacon, Eric Granger

Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild

Read the original on arXiv Computer Vision →

The paper compares text‑based and feature‑based models for recognizing compound emotions in real‑world videos. It proposes textualizing non‑verbal cues from audio and visual modalities into text to leverage large language models, while feature‑based models directly combine extracted multimodal features. Experiments on the C‑EXPR‑DB dataset show that feature‑based models outperform textualization in the wild, though textual models can excel when rich transcripts are available.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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