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Learning from Label Relationships in Human Affect

A framework aiming to improve generalization performance and mitigate overfitting in deep learning methodologies in automated human affect and mental state estimation by introducing a novel relational loss for multilabel regression and ordinal problems, and employing a two-stage attention architecture to leverage temporal context from neighbouring clips.