Unsupervised domain adaptation for human pose action recognition

Abstract

Personalized human action recognition is important to give accurate feedback about motion patterns, but there is likely no labeled data available to update the model in a supervised way. Unsupervised domain adaptation can solve this problem by closing the gap between seen data and new unseen users. We test several domain adaptation techniques and compare them with each other on this task. We show that all tested techniques improve on the model without any domain adaptation and are only trained on labeled source data. We add multiple improvements by designing a better feature representation tailored to the new user. These improvements include added contrastive loss and varying the backbone encoder. We would need between 30% and 40% labeled data of the new user to get the same results.