  Multi-task learning has proven to be effective in improving the performance
of correlated tasks. Most of the existing methods use a backbone to extract
initial features with independent branches for each task, and the exchange of
information between the branches usually occurs through the concatenation or
sum of the feature maps of the branches. However, this type of information
exchange does not directly consider the local characteristics of the image nor
the level of importance or correlation between the tasks. In this paper, we
propose a semantic segmentation method, MTLSegFormer, which combines multi-task
learning and attention mechanisms. After the backbone feature extraction, two
feature maps are learned for each task. The first map is proposed to learn
features related to its task, while the second map is obtained by applying
learned visual attention to locally re-weigh the feature maps of the other
tasks. In this way, weights are assigned to local regions of the image of other
tasks that have greater importance for the specific task. Finally, the two maps
are combined and used to solve a task. We tested the performance in two
challenging problems with correlated tasks and observed a significant
improvement in accuracy, mainly in tasks with high dependence on the others.
