{"type":"ScholarlyArticle","title":"Towards Regularized Mixture of Predictions for Class-Imbalanced Semi-Supervised Facial Expression Recognition","authors":[{"name":"Hangyu Li","slug":"hangyu-li","given":"Hangyu","family":"Li","affiliation":""},{"name":"Yixin Zhang","slug":"yixin-zhang","given":"Yixin","family":"Zhang","affiliation":""},{"name":"Jiangchao Yao","slug":"jiangchao-yao","given":"Jiangchao","family":"Yao","affiliation":""},{"name":"Nannan Wang","slug":"nannan-wang","given":"Nannan","family":"Wang","affiliation":""},{"name":"Bo Han","slug":"bo-han","given":"Bo","family":"Han","affiliation":""}],"abstract":"Semi-supervised facial expression recognition (SSFER) effectively assigns pseudo-labels to confident unlabeled samples when only limited emotional annotations are available. Existing SSFER methods are typically built upon an assumption of the class-balanced distribution. However, they are far from real-world applications due to biased pseudo-labels caused by class imbalance. To alleviate this issue, we propose Regularized Mixture of Predictions (ReMoP), a simple yet effective method to generate high-quality pseudo-labels for imbalanced samples. Specifically, we first integrate feature similarity into the linear prediction to learn a mixture of predictions. Furthermore, we introduce a class regularization term that constrains the feature geometry to mitigate imbalance bias. Being practically simple, our method can be integrated with existing semi-supervised learning and SSFER methods to tackle the challenge associated with class-imbalanced SSFER effectively. Extensive experiments on four facial expression datasets demonstrate the effectiveness of the proposed method across various imbalanced conditions. The source code is made publicly available at https://github.com/hangyu94/ReMoP.","doi":"10.24963/ijcai.2025/154","url":"https://ijcai.org/proceedings/2025/154","pdf":"https://ijcai.org/proceedings/2025/0154.pdf","markdown":"https://ijcai.org/proceedings/2025/154.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/154","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":154,"pages":{"first":1377,"last":1385},"keywords":["Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Biometrics, face, gesture and pose recognition","Humans and AI: HAI: Human-computer interaction"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}