ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2018
Learning Facial Action Units from Web Images with Scalable Weakly Supervised Clustering.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Unsupervised Facial Action Representation Learning by Temporal Prediction.Frontiers in neurorobotics · 2022Article
- Indirectly-Supervised Anomaly Detection of Clinically-Meaningful Health Events from Smart Home Data.ACM transactions on intelligent systems and technology · 2021Article
- Progressive Multi-Scale Vision Transformer for Facial Action Unit Detection.Frontiers in neurorobotics · 2021Article
- Synthetic Expressions are Better Than Real for Learning to Detect Facial Actions.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2021Article
- D-PAttNet: Dynamic Patch-Attentive Deep Network for Action Unit Detection.Frontiers in computer science · 2019Article
- The promises and perils of automated facial action coding in studying children's emotions.Developmental psychology · 2019Article
- Automatic Micro-Expression Analysis: Open Challenges.Frontiers in psychology · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We present a scalable weakly supervised clustering approach to learn facial action units (AUs) from large, freely available web images. Unlike most existing methods (e.g., CNNs) that rely on fully annotated data, our method exploits web images with inaccurate annotations. Specifically, we derive a weakly-supervised spectral algorithm that learns an embedding space to couple image appearance and semantics. The algorithm has efficient gradient update, and scales up to large quantities of images with a stochastic extension. With the learned embedding space, we adopt rank-order clustering to identify groups of visually and semantically similar images, and re-annotate these groups for training AU classifiers. Evaluation on the 1 millon EmotioNet dataset demonstrates the effectiveness of our approach: (1) our learned annotations reach on average 91.3% agreement with human annotations on 7 common AUs, (2) classifiers trained with re-annotated images perform comparably to, sometimes even better than, its supervised CNN-based counterpart, and (3) our method offers intuitive outlier/noise pruning instead of forcing one annotation to every image. Code is available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.