Multi-Task Learning for Visual Scene Understanding

• URL: http://arxiv.org/abs/2203.14896v1
• Date: Mon, 28 Mar 2022 16:57:58 GMT
• Title: Multi-Task Learning for Visual Scene Understanding
• Authors: Simon Vandenhende
• Abstract summary: This thesis is concerned with multi-task learning in the context of computer vision. We propose several methods that tackle important aspects of multi-task learning. The results show several advances in the state-of-the-art of multi-task learning.
• Score: 7.191593674138455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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