Multi-Task Learning for Visual Scene Understanding

Simon Vandenhende

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arXiv · 2203.14896

Multi-Task Learning for Visual Scene Understanding

Simon Vandenhende

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Abstract

Despite the recent progress in deep learning, most approaches still go for a silo-like solution, focusing on learning each task in isolation: training a separate neural network for each individual task. Many real-world problems, however, call for a multi-modal approach and, therefore, for multi-tasking models. Multi-task learning (MTL) aims to leverage useful information across tasks to improve the generalization capability of a model. This thesis is concerned with multi-task learning in the context of computer vision. First, we review existing approaches for MTL. Next, we propose several methods that tackle important aspects of multi-task learning. The proposed methods are evaluated on various benchmarks. The results show several advances in the state-of-the-art of multi-task learning. Finally, we discuss several possibilities for future work.

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BibTeX RIS

Simon Vandenhende. 2022-03-28. Multi-Task Learning for Visual Scene Understanding. https://arxiv.org/abs/2203.14896

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