Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning

Sánchez, Ernesto Sánchez; Bulat, Adrian; Zaganidis, Anestis; Tzimiropoulos, Georgios

doi:10.1007/978-3-030-69541-5_7

Cited by 3 publications

(3 citation statements)

References 47 publications

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“…Furthermore, it is known that network is quite sensitive at beginning when it is trained from the scratch. This was established in the paper [31], which demonstrated that vanishing gradients occur in an unstable training process when a deep neural network is not properly initialized, causing the training process to be unstable. To identify qualities that are complementary to one another, Chen et al [4] introduced DualCheXNet, a dual asymmetric feature learning network.…”

Section: Related Workmentioning

confidence: 97%

Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images

Ahmad,

Malik,

Qamar

et al. 2023

Diagnostics

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Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. The suggested study can assist radiologists in more swiftly diagnosing thorax disorders and in the rapid airport screening of patients with a thorax disease, such as pneumonia. This paper focuses on automatically detecting and localizing thorax disease using chest X-ray images. It provides accurate detection and localization using DenseNet-121 which is foundation of our proposed framework, called Z-Net. The proposed framework utilizes the weighted cross-entropy loss function (W-CEL) that manages class imbalance issue in the ChestX-ray14 dataset, which helped in achieving the highest performance as compared to the previous models. The 112,120 images contained in the ChestX-ray14 dataset (60,412 images are normal, and the rest contain thorax diseases) were preprocessed and then trained for classification and localization. This work uses computer-aided diagnosis (CAD) system that supports development of highly accurate and precise computer-aided systems. We aim to develop a CAD system using a deep learning approach. Our quantitative results show high AUC scores in comparison with the latest research works. The proposed approach achieved the highest mean AUC score of 85.8%. This is the highest accuracy documented in the literature for any related model.

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Section: Related Workmentioning

confidence: 97%

Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images

Ahmad,

Malik,

Qamar

et al. 2023

Diagnostics

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“…The Thirty-Eighth AAAI Conference on Artificial Intelligence intensity estimation has become a practical and effective solution (Zhao et al 2016;Zhang et al 2018c,b;Wang et al 2019;Zhang et al 2019a;Sanchez et al 2020).…”

Section: Semi-supervised Au Intensity Estimationmentioning

confidence: 99%

Trend-Aware Supervision: On Learning Invariance for Semi-supervised Facial Action Unit Intensity Estimation

Chen,

Zhang,

Wang

et al. 2024

AAAI

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With the increasing need for facial behavior analysis, semi-supervised AU intensity estimation using only keyframe annotations has emerged as a practical and effective solution to relieve the burden of annotation. However, the lack of annotations makes the spurious correlation problem caused by AU co-occurrences and subject variation much more prominent, leading to non-robust intensity estimation that is entangled among AUs and biased among subjects. We observe that trend information inherent in keyframe annotations could act as extra supervision and raising the awareness of AU-specific facial appearance changing trends during training is the key to learning invariant AU-specific features. To this end, we propose Trend-AwareSupervision (TAS), which pursues three kinds of trend awareness, including intra-trend ranking awareness, intra-trend speed awareness, and inter-trend subject awareness. TAS alleviates the spurious correlation problem by raising trend awareness during training to learn AU-specific features that represent the corresponding facial appearance changes, to achieve intensity estimation invariance. Experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, show the effectiveness of each kind of awareness. And under trend-aware supervision, the performance can be improved without extra computational or storage costs during inference.

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“…It is worth mentioning that the newer directions for estimating AU intensity seek learning models with little or no supervision, including work done by Sanchez et al [168], Wang and Peng [196], Wang et al [195], and Zhang et al [210].…”

Section: Jointly Estimating Landmark Detection and Action Unit Intensitymentioning

confidence: 99%

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Pourebadi

Riek

2022

ACM Trans. Comput. Healthcare

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Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.

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Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning

Cited by 3 publications

References 47 publications

Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images

Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images

Trend-Aware Supervision: On Learning Invariance for Semi-supervised Facial Action Unit Intensity Estimation

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

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