Semi-supervised Multi-task Learning for Semantics and Depth
- URL: http://arxiv.org/abs/2110.07197v1
- Date: Thu, 14 Oct 2021 07:43:39 GMT
- Title: Semi-supervised Multi-task Learning for Semantics and Depth
- Authors: Yufeng Wang, Yi-Hsuan Tsai, Wei-Chih Hung, Wenrui Ding, Shuo Liu,
Ming-Hsuan Yang
- Abstract summary: Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance.
We propose the Semi-supervised Multi-Task Learning (MTL) method to leverage the available supervisory signals from different datasets.
We present a domain-aware discriminator structure with various alignment formulations to mitigate the domain discrepancy issue among datasets.
- Score: 88.77716991603252
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-Task Learning (MTL) aims to enhance the model generalization by sharing
representations between related tasks for better performance. Typical MTL
methods are jointly trained with the complete multitude of ground-truths for
all tasks simultaneously. However, one single dataset may not contain the
annotations for each task of interest. To address this issue, we propose the
Semi-supervised Multi-Task Learning (SemiMTL) method to leverage the available
supervisory signals from different datasets, particularly for semantic
segmentation and depth estimation tasks. To this end, we design an adversarial
learning scheme in our semi-supervised training by leveraging unlabeled data to
optimize all the task branches simultaneously and accomplish all tasks across
datasets with partial annotations. We further present a domain-aware
discriminator structure with various alignment formulations to mitigate the
domain discrepancy issue among datasets. Finally, we demonstrate the
effectiveness of the proposed method to learn across different datasets on
challenging street view and remote sensing benchmarks.
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