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。

密集型多任务学习的跨任务注意机制

Cross-task Attention Mechanism for Dense Multi-task Learning

3D semantic scene completion and 2D semantic segmentation are two tightly
correlated tasks that are both essential for indoor scene understanding,
because they predict the same semantic classes, using positively correlated
high-level features. Current methods use 2D features extracted from early-fused
RGB-D images for 2D segmentation to improve 3D scene completion. We argue that
this sequential scheme does not ensure these two tasks fully benefit each
other, and present an Iterative Mutual Enhancement Network (IMENet) to solve
them jointly, which interactively refines the two tasks at the late prediction
stage. Specifically, two refinement modules are developed under a unified
framework for the two tasks. The first is a 2D Deformable Context Pyramid (DCP)
module, which receives the projection from the current 3D predictions to refine
the 2D predictions. In turn, a 3D Deformable Depth Attention (DDA) module is
proposed to leverage the reprojected results from 2D predictions to update the
coarse 3D predictions. This iterative fusion happens to the stable high-level
features of both tasks at a late stage. Extensive experiments on NYU and NYUCAD
datasets verify the effectiveness of the proposed iterative late fusion scheme,
and our approach outperforms the state of the art on both 3D semantic scene
completion and 2D semantic segmentation.

本文提出了一种 Iterative Mutual Enhancement Network (IMENet)，能够联合处理 3D 语义场景完成和 2D 语义分割任务，通过两个精细化模块 Deformable Context Pyramid 和 Deformable Depth Attention，在两个任务的高级特征上进行交互迭代优化融合，提高了模型在 NYU 和 NYUCAD 数据集上的泛化能力与模型性能。