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Found 5 papers, 2 papers with code
Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition
Machine learning models automatically learn discriminative features from the data, and are therefore susceptible to learn strongly-correlated biases, such as using protected attributes like gender and race.
Facial Expression Recognition
Facial Expression Recognition (FER)
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Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification
Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them.
Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition
Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in daily life.
Critically examining the Domain Generalizability of Facial Expression Recognition models
Using a state-of-the-art model with twelve datasets (six collected in-lab and six ``in-the-wild"), we conduct extensive round-robin-style experiments to evaluate classification accuracies when given new data from an unseen dataset.
Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons
We found that existing approaches become less accurate when the underlying process is complex and generates large varieties of patterns in the time series.
