Few-Shot Learning for Personalised Automated Pain Assessment
In this work, we evaluate Few-Shot Learning, a sub-area of Meta-Learning, as an approach to personalisation in automated pain assessment.
Key points
- Pain perception varies substantially across individuals, making it difficult for population-based classifiers to generalise across all subjects in a dataset.
- One way to account for subject variability is to train personalised classifiers.
- We evaluate our method on the BioVid Pain Database, the SenseEmotion Database, and the PainMonit Experimental Dataset (PMED), reaching 85.75% and 35.49% accuracy on BioVid and 82.37% and 41.88% on SenseEmotion in the binary and multi-class settings under a Leave-One-Subject-Out CV protocol respectively, and 90.47% on PMED, for which only a binary benchmark exists.
- Our results suggest that support-conditioned few-shot adaptation can improve average performance under inter-subject variability.
Sources (1)
- [1]Few-Shot Learning for Personalised Automated Pain AssessmentarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 08:52 AM
In this work, we evaluate Few-Shot Learning, a sub-area of Meta-Learning, as an approach to personalisation in automated pain assessment.
Pain perception varies substantially across individuals, making it difficult for population-based classifiers to generalise across all subjects in a dataset.
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