We propose a novel regularization method that can be plugged into diverse prior Multi-task Learning architectures for dense vision problems including both convolutional and transformer networks where the structured 3D-aware regularizer interfaces multiple tasks through the projection of features extracted from an image encoder to a shared 3D feature space and decodes them into their task output space through differentiable rendering.
Multi-task Learning with 3D-Aware Regularization,
Wei-Hong Li, Steven McDonagh, Ales Leonardis, Hakan Bilen,
ICLR 2024 (arXiv 2310.00986)
- January'24, Our paper is accepted to ICLR'24! Code will be available soon!
For any question, you can contact Wei-Hong Li.
If you use this code, please cite our papers:
@inproceedings{li20243dawaremtl,
author = {Li, Wei-Hong and McDonagh, Steven and Leonardis, Ales and Bilen, Hakan},
title = {Multi-task Learning with 3D-Aware Regularization},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {May},
year = {2024}
}