  Compared to RGB semantic segmentation, RGBD semantic segmentation can achieve
better performance by taking depth information into consideration. However, it
is still problematic for contemporary segmenters to effectively exploit RGBD
information since the feature distributions of RGB and depth (D) images vary
significantly in different scenes. In this paper, we propose an Attention
Complementary Network (ACNet) that selectively gathers features from RGB and
depth branches. The main contributions lie in the Attention Complementary
Module (ACM) and the architecture with three parallel branches. More precisely,
ACM is a channel attention-based module that extracts weighted features from
RGB and depth branches. The architecture preserves the inference of the
original RGB and depth branches, and enables the fusion branch at the same
time. Based on the above structures, ACNet is capable of exploiting more
high-quality features from different channels. We evaluate our model on
SUN-RGBD and NYUDv2 datasets, and prove that our model outperforms
state-of-the-art methods. In particular, a mIoU score of 48.3\% on NYUDv2 test
set is achieved with ResNet50. We will release our source code based on PyTorch
and the trained segmentation model at https://github.com/anheidelonghu/ACNet.
