Detailed info
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
| Authors | Alessandro Conti, Paolo Rota, Yiming Wang, Elisa Ricci |
| Title | Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition |
| Abstract | Automatically understanding emotions from visual data is a fundamental task for human behaviour understanding. While models devised for Facial Expression Recognition (FER) have demonstrated excellent performances on many datasets, they often suffer from severe performance degradation when trained and tested on different datasets due to domain shift. In addition, as face images are considered highly sensitive data, the accessibility to large-scale datasets for model training is often denied. In this work, we tackle the above-mentioned problems by proposing the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for FER. Our method exploits self-supervised pretraining to learn good feature representations from the target data and proposes a novel and robust cluster-level pseudo-labelling strategy that accounts for in-cluster statistics. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER, and is on par with methods addressing FER in the UDA setting |
| ISBN | TBA |
| Conference | The 33rd British Machine Vision Conference (BMVC 2022) |
| Date | 21 – 24/11/2022 |
| Location | London, UK |
| Year of Publication | 2022 |
| Url | https://zenodo.org/record/7296310 |
| DOI | 10.5281/zenodo.7296310 |
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Funding
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.
