Kardelen Ceren CS398 – Project in Computer Science I, 8 ECTS, Spring 2023
EPFL, Visual Intelligence for Transportation (VITA) Laboratory
Professor: Alexandre Alahi, Supervisor: Megh Shukla
Code built upon https://github.com/meghshukla/ActiveLearningForHumanPose/tree/main
This project aims to develop an incremental learning approach for human pose estimation. Traditional models struggle to adapt to new poses without extensive retraining. To overcome this challenge, we explore incremental learning techniques that enable models to learn from new data while retaining previous knowledge, reducing catastrophic forgetting. We modify the Stacked Hourglass model, incorporate task heads, and investigate regularization methods. By comparing different incremental learning strategies to baselines on the MPII and LSP datasets, we highlight the importance of different incremental learning strategies in improving human pose estimation algorithms.
MPII dataset can be found here. LSP can be found here. After downloading, add their "images" folder to this repository's data folder, under appropriate name.