Multimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling
Expression recognition in in-the-wild video data remains challenging due to substantial variations in facial appearance, background conditions, audio noise, and the inherently dynamic nature of human affect. Relying on a single modality, such as facial expressions or speech, is often insufficient for capturing these complex emotional cues. To address this limitation, we propose a multimodal emotion recognition framework for the Expression (EXPR) task in the 10th Affective Behavior Analysis in-the-wild (ABAW) Challenge. Our framework builds on large-scale pre-trained models for visual and audio representation learning and integrates them in a unified multimodal architecture. To better capt...
| Authors | Junhyeong Byeon, Jeongyeol Kim, Sejoon Lim |
|---|---|
| Primary category | cs.CV |
| Published | 12 March 2026 |
| Last updated at source | 18 March 2026 |
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