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Fighting over-fitting with quantization for learning deep neural networks on noisy labels
The rising performance of deep neural networks is often empirically attributed to an increase in the available computational power, which allows complex models to be trained upon large amounts of annotated data.
Fighting noise and imbalance in Action Unit detection problems
However, applying label smoothing as it is may aggravate imbalance-based pre-existing under-confidence issue and degrade performance.
Multi-Task Transformer with uncertainty modelling for Face Based Affective Computing
From a learning point of view we use an uncertainty weighted loss for modelling the difference of stochasticity between the three tasks annotations.
Multi-label Transformer for Action Unit Detection
Action Unit (AU) Detection is the branch of affective computing that aims at recognizing unitary facial muscular movements.
Multi-Order Networks for Action Unit Detection
MONET uses a differentiable order selection to jointly learn task-wise modules with their optimal chaining order.
