Active Transfer Learning for Efficient Video-Specific Human Pose
  Estimation
Hiromu Taketsugu Norimichi Ukita
Human Pose (HP) estimation is actively researched because of its wide range
of applications. However, even estimators pre-trained on large datasets may not
perform satisfactorily due to a domain gap between the training and test data.
To address this issue, we present our approach combining Active Learning (AL)
and Transfer Learning (TL) to adapt HP estimators to individual video domains
efficiently. For efficient learning, our approach quantifies (i) the estimation
uncertainty based on the temporal changes in the estimated heatmaps and (ii)
the unnaturalness in the estimated full-body HPs. These quantified criteria are
then effectively combined with the state-of-the-art representativeness
criterion to select uncertain and diverse samples for efficient HP estimator
learning. Furthermore, we reconsider the existing Active Transfer Learning
(ATL) method to introduce novel ideas related to the retraining methods and
Stopping Criteria (SC). Experimental results demonstrate that our method
enhances learning efficiency and outperforms comparative methods. Our code is
publicly available at: https://github.com/ImIntheMiddle/VATL4Pose-WACV2024