  In this paper, we present our deep attention-based classification (DABC)
network for robust single image depth prediction, in the context of the Robust
Vision Challenge 2018 (ROB 2018). Unlike conventional depth prediction, our
goal is to design a model that can perform well in both indoor and outdoor
scenes with a single parameter set. However, robust depth prediction suffers
from two challenging problems: a) How to extract more discriminative features
for different scenes (compared to a single scene)? b) How to handle the large
differences of depth ranges between indoor and outdoor datasets? To address
these two problems, we first formulate depth prediction as a multi-class
classification task and apply a softmax classifier to classify the depth label
of each pixel. We then introduce a global pooling layer and a channel-wise
attention mechanism to adaptively select the discriminative channels of
features and to update the original features by assigning important channels
with higher weights. Further, to reduce the influence of quantization errors,
we employ a soft-weighted sum inference strategy for the final prediction.
Experimental results on both indoor and outdoor datasets demonstrate the
effectiveness of our method. It is worth mentioning that we won the 2-nd place
in single image depth prediction entry of ROB 2018, in conjunction with IEEE
Conference on Computer Vision and Pattern Recognition (CVPR) 2018.
