  Incorporating various modes of information into the machine learning
procedure is becoming a new trend. And data from various source can provide
more information than single one no matter they are heterogeneous or
homogeneous. Existing deep learning based algorithms usually directly
concatenate features from each domain to represent the input data. Seldom of
them take the quality of data into consideration which is a key issue in
related multimodal problems. In this paper, we propose an efficient
quality-aware deep neural network to model the weight of data from each domain
using deep reinforcement learning (DRL). Specifically, we take the weighting of
each domain as a decision-making problem and teach an agent learn to interact
with the environment. The agent can tune the weight of each domain through
discrete action selection and obtain a positive reward if the saliency results
are improved. The target of the agent is to achieve maximum rewards after
finished its sequential action selection. We validate the proposed algorithms
on multimodal saliency detection in a coarse-to-fine way. The coarse saliency
maps are generated from an encoder-decoder framework which is trained with
content loss and adversarial loss. The final results can be obtained via
adaptive weighting of maps from each domain. Experiments conducted on two kinds
of salient object detection benchmarks validated the effectiveness of our
proposed quality-aware deep neural network.
