Multi-task learning has recently become a promising solution for a
comprehensive understanding of complex scenes. Not only being memory-efficient,
multi-task models with an appropriate design can favor exchange of
complementary signals across tasks. In this work, we jointly address 2D
semantic segmentation, and two geometry-related tasks, namely dense depth,
surface normal estimation as well as edge estimation showing their benefit on
indoor and outdoor datasets. We propose a novel multi-task learning
architecture that exploits pair-wise cross-task exchange through
correlation-guided attention and self-attention to enhance the average
representation learning for all tasks. We conduct extensive experiments
considering three multi-task setups, showing the benefit of our proposal in
comparison to competitive baselines in both synthetic and real benchmarks. We
also extend our method to the novel multi-task unsupervised domain adaptation
setting. Our code is available at this https URL.

该研究介绍了一种多任务学习架构，通过相关性引导的注意力和自我关注增强平均表示学习，以通过 2D 语义分割和几何相关的任务来综合理解复杂场景，并在室内和室外数据集上展示了其好处。