Chen, Xueting;
Yan, Yan;
Xue, Jing-Hao;
Shu, Chang;
Wang, Hanzi;
(2025)
Hyperbolic Self-Paced Multi-Expert Network for Cross-Domain Few-Shot Facial Expression Recognition.
IEEE Transactions on Image Processing
10.1109/tip.2025.3612281.
(In press).
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Abstract
Recently, cross-domain few-shot facial expression recognition (CF-FER), which identifies novel compound expressions with a few images in the target domain by using the model trained only on basic expressions in the source domain, has attracted increasing attention. Generally, existing CF-FER methods leverage the multi-dataset to increase the diversity of the source domain and alleviate the discrepancy between the source and target domains. However, these methods learn feature embeddings in the Euclidean space without considering imbalanced expression categories and imbalanced sample difficulty in the multi-dataset. Such a way makes the model difficult to capture hierarchical relationships of facial expressions, resulting in inferior transferable representations. To address these issues, we propose a hyperbolic self-paced multi-expert network (HSM-Net), which contains multiple mixture-of-experts (MoE) layers located in the hyperbolic space, for CF-FER. Specifically, HSM-Net collaboratively trains multiple experts in a self-distillation manner, where each expert focuses on learning a subset of expression categories from the multi-dataset. Based on this, we introduce a hyperbolic self-paced learning (HSL) strategy that exploits sample difficulty to adaptively train the model from easy-to-hard samples, greatly reducing the influence of imbalanced expression categories and imbalanced sample difficulty. Our HSM-Net can effectively model rich hierarchical relationships of facial expressions and obtain a highly transferable feature space. Extensive experiments on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed method over several state-of-the-art methods. Code will be released at https://github.com/cxtjl/HSM-Net.
| Type: | Article |
|---|---|
| Title: | Hyperbolic Self-Paced Multi-Expert Network for Cross-Domain Few-Shot Facial Expression Recognition |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tip.2025.3612281 |
| Publisher version: | https://doi.org/10.1109/tip.2025.3612281 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Compound facial expression recognition, Crossdomain few-shot learning, Self-paced learning, Mixture-of-experts, Hierarchical representation learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10214691 |
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