CLAIP-Emo: Parameter-Efficient Adaptation of Language-supervised models for In-the-Wild Audiovisual Emotion Recognition
If you find this work helpful, please consider citing:
@article{chen2025claip,
title={CLAIP-Emo: Parameter-Efficient Adaptation of Language-supervised models for In-the-Wild Audiovisual Emotion Recognition},
author={Chen, Yin and Li, Jia and Hu, Jinpeng and Hu, Zhenzhen and Hong, Richang},
journal={arXiv preprint arXiv:2509.14527},
year={2025}
}
@ARTICLE{10663980,
author={Chen, Yin and Li, Jia and Shan, Shiguang and Wang, Meng and Hong, Richang},
journal={IEEE Transactions on Affective Computing},
title={From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos},
year={2024},
volume={},
number={},
pages={1-15},
keywords={Adaptation models;Videos;Computational modeling;Feature extraction;Transformers;Task analysis;Face recognition;Dynamic facial expression recognition;emotion ambiguity;model adaptation;transfer learning},
doi={10.1109/TAFFC.2024.3453443}}
@article{chen2024static,
title={Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data},
author={Chen, Yin and Li, Jia and Zhang, Yu and Hu, Zhenzhen and Shan, Shiguang and Wang, Meng and Hong, Richang},
journal={IEEE Transactions on Affective Computing},
doi={10.1109/TAFFC.2025.3623135}}
}