  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.
