Self-distillation and uncertainty boosting self-supervised monocular depth estimation

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Zhou, Hang, Greenwood, David, Taylor, Sarah and Mackiewicz, Michal ORCID: https://orcid.org/0000-0002-8777-8880 (2022) Self-distillation and uncertainty boosting self-supervised monocular depth estimation. In: THe 33rd British Machine Vision Conference Proceedings. UNSPECIFIED.

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Abstract

For self-supervised monocular depth estimation (SDE), recent works have introduced additional learning objectives, for example semantic segmentation, into the training pipeline and have demonstrated improved performance. However, such multi-task learning frameworks require extra ground truth labels, neutralising the biggest advantage of self-supervision. In this paper, we propose SUB-Depth to overcome these limitations. Our main contribution is that we design an auxiliary self-distillation scheme and incorporate it into the standard SDE framework, to take advantage of multi-task learning without labelling cost. Then, instead of using a simple weighted sum of the multiple objectives, we employ generative task-dependent uncertainty to weight each task in our proposed training framework. We present extensive evaluations on KITTI to demonstrate the improvements achieved by training a range of existing networks using the proposed framework, and we achieve state-of-the-art performance on this task.

Item Type:	Book Section
Additional Information:	© 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic form.
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Colour and Imaging Lab
Related URLs:	https://bmvc2022.mpi-inf.mpg.de/0007.pdf
Depositing User:	LivePure Connector
Date Deposited:	10 May 2023 14:30
Last Modified:	02 Jun 2024 01:31
URI:	https://ueaeprints.uea.ac.uk/id/eprint/92014
DOI:

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