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Recent studies on the automatic detection of facial action unit (AU) have extensively relied on large-sized annotations.
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Peng, G., Wang, S.: Weakly supervised facial action unit recognition through adversarial training. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 2188–2196 (2018)
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Li, G., Zhu, X., Zeng, Y., Wang, Q., Lin, L.: Semantic relationships guided representation learning for facial action unit recognition. Proceedings of the AAAI Conference on Artificial Intelligence (2019)
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Li, Y., Zeng, J., Shan, S., Chen, X.: Self-supervised representation learning from videos for facial action unit detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Niu, X., Han, H., Shan, S., Chen, X.: Multi-label co-regularization for semi-supervised facial action unit recognition. In: NeurIPS (2019)
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Niu, X., et al.: Local relationship learning with person-specific shape regularization for facial action unit detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Peng, G., Wang, S.: Dual semi-supervised learning for facial action unit recognition. Proceedings of the AAAI Conference on Artificial Intelligence (2019)
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Shao, Z., et al.: Facial action unit detection using attention and relation learning. IEEE Transactions on Affective Computing p. 1–1 (2019)
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Zhang, Y., Wu, B., Dong, W., Li, Z., Liu, W., Hu, B.G., Ji, Q.: Joint representation and estimator learning for facial action unit intensity estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
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2021
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Li, X., Li, Z., Yang, H., Zhao, G., Yin, L.: Your “attention” deserves attention: A self-diversified multi-channel attention for facial action analysis. In: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021) (2021)
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Singh, A., Chakraborty, O., Varshney, A., Panda, R., Feris, R., Saenko, K., Das, A.: Semi-supervised action recognition with temporal contrastive learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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Song, T., Chen, L., Zheng, W., Ji, Q.: Uncertain graph neural networks for facial action unit detection. Proceedings of the AAAI Conference on Artificial Intelligence pp. 5993–6001 (2021)
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Song, T., Cui, Z., Zheng, W., Ji, Q.: Hybrid message passing with performance-driven structures for facial action unit detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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Song, T., Cui, Z., Zheng, W., Ji, Q.: Hybrid message passing with performance-driven structures for facial action unit detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
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Tang, Y., Zeng, W., Zhao, D., Zhang, H.: Piap-df: Pixel-interested and anti person-specific facial action unit detection net with discrete feedback learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
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Wang, X., Zhang, S., Qing, Z., Shao, Y., Gao, C., Sang, N.: Self-supervised learning for semi-supervised temporal action proposal. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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Wang, X., Zhang, S., Qing, Z., Shao, Y., Gao, C., Sang, N.: Self-supervised learning for semi-supervised temporal action proposal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1905–1914 (June 2021)
2021
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Yang, H., Yin, L., Zhou, Y., Gu, J.: Exploiting semantic embedding and visual feature for facial action unit detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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