  This paper addresses the task of designing a modular neural network
architecture that jointly solves different tasks. As an example we use the
tasks of depth estimation and semantic segmentation given a single RGB image.
The main focus of this work is to analyze the cross-modality influence between
depth and semantic prediction maps on their joint refinement. While most
previous works solely focus on measuring improvements in accuracy, we propose a
way to quantify the cross-modality influence. We show that there is a
relationship between final accuracy and cross-modality influence, although not
a simple linear one. Hence a larger cross-modality influence does not
necessarily translate into an improved accuracy. We find that a beneficial
balance between the cross-modality influences can be achieved by network
architecture and conjecture that this relationship can be utilized to
understand different network design choices. Towards this end we propose a
Convolutional Neural Network (CNN) architecture that fuses the state of the
state-of-the-art results for depth estimation and semantic labeling. By
balancing the cross-modality influences between depth and semantic prediction,
we achieve improved results for both tasks using the NYU-Depth v2 benchmark.
