The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition
Abstract
Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity. In this paper, we proposes a solution, named DMUE, to address the problem of annotation ambiguity from two perspectives: the latent Distribution Mining and the pairwise Uncertainty Estimation. For the former, an auxiliary multi-branch learning framework is introduced to better mine and describe the latent distribution in the label space. For the latter, the pairwise relationship of semantic feature between instances are fully exploited to estimate the ambiguity extent in the instance space. The proposed method is independent to the backbone architectures, and brings no extra burden for inference. The experiments are conducted on the popular real-world benchmarks and the synthetic noisy datasets. Either way, the proposed DMUE stably achieves leading performance.
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Cites
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
SIFT: predicting amino acid changes that affect protein function
Ng, P. C. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg509 · Cited by 6,577
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Shan, C., Gong, S., & McOwan, P. W. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.005 · Cited by 2,300
SphereFace: Deep Hypersphere Embedding for Face Recognition
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.713 · Cited by 2,280
Challenges in Representation Learning: A Report on Three Machine Learning Contests
Goodfellow, I. J., Erhan, D., Carrier, P. L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., et al. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-42051-1_16 · Cited by 1,719
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
Li, S., Deng, W., & Du, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.277 · Cited by 1,470
Temporal Ensembling for Semi-Supervised Learning
Laine, S., & Aila, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.02242 · Cited by 1,395
Similarity-Preserving Knowledge Distillation
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Symmetric Cross Entropy for Robust Learning With Noisy Labels
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Cited by
Face2Exp: Combating Data Biases for Facial Expression Recognition
Zeng, D., Lin, Z., Yan, X., Liu, Y., Wang, F., & Tang, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01965 · Cited by 141
Adaptive Multilayer Perceptual Attention Network for Facial Expression Recognition
Liu, H., Cai, H., Lin, Q., Li, X., & Xiao, H. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3165321 · Cited by 129
A Hybrid Explainable AI Framework Applied to Global and Local Facial Expression Recognition
Deramgozin, M., Jovanovic, S., Rabah, H., & Ramzan, N. (2021) · 2021 IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist50367.2021.9651357 · Cited by 22
SAFER: Situation Aware Facial Emotion Recognition
Palash, M., & Bhargava, B. (2023) · arXiv · doi:10.48550/arxiv.2306.09372 · Cited by 2
“[23] used latent distribution mining and pairwise uncertainty estimation for facial emotion recognition.”
Method
Metadata
- Authors
- She, J., Hu, Y., Shi, H., Wang, J., Shen, Q., & Mei, T.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 6244-6253
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3173787706
- Cited by
- 264
- References
- 49
- FWCI
- 51.98
- Type
- conference paper
- Language
- en
- Open access
- closed
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- She, J., Hu, Y., Shi, H., Wang, J., Shen, Q., & Mei, T. (2021). Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.00618
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