Lee, JunGyu Lee, Kunyoung Park, Hae sol KIM, IG JAE Nam, Gi Pyo 2025-11-28T05:00:25Z 2025-11-28T05:00:25Z 2025-11-13 2025-06-11 https://pubs.kist.re.kr/handle/201004/153712 Facial Expression Recognition (FER) plays a crucial role in human affective analysis and has been widely applied in computer vision tasks such as human-computer interaction and psychological assessment. The 8th Affective Behavior Analysis in-the-Wild (ABAW) Challenge aims to assess human emotions using the video-based Aff-Wild2 dataset. This challenge includes various tasks, including the video-based EXPR recognition track, which is our primary focus. In this paper, we demonstrate that addressing label ambiguity and class imbalance, which are known to cause performance degradation, can lead to meaningful performance improvements. Specifically, we propose Videobased Noise-aware Adaptive Weighting (V-NAW), which adaptively assigns importance to each frame in a clip to address label ambiguity and effectively capture temporal variations in facial expressions. Furthermore, we introduce a simple and effective augmentation strategy to reduce redundancy between consecutive frames, which is a primary cause of overfitting. Through extensive experiments, we validate the effectiveness of our approach, demonstrating significant improvements in video-based FER performance. IEEE/CVF V-NAW: Video-Based Noise-Aware Adaptive Weighting for Facial Expression Recognition Conference 10.1109/cvprw67362.2025.00561 1 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), v.-, pp.5643 - 5650 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) - 5643 5650 US Nashville, TN, USA 2025-06-11 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)