  The goal of multi-modal learning is to use complimentary information on the
relevant task provided by the multiple modalities to achieve reliable and
robust performance. Recently, deep learning has led significant improvement in
multi-modal learning by allowing for the information fusion in the intermediate
feature levels. This paper addresses a problem of designing robust deep
multi-modal learning architecture in the presence of imperfect modalities. We
introduce deep fusion architecture for object detection which processes each
modality using the separate convolutional neural network (CNN) and constructs
the joint feature map by combining the intermediate features from the CNNs. In
order to facilitate the robustness to the degraded modalities, we employ the
gated information fusion (GIF) network which weights the contribution from each
modality according to the input feature maps to be fused. The weights are
determined through the convolutional layers followed by a sigmoid function and
trained along with the information fusion network in an end-to-end fashion. Our
experiments show that the proposed GIF network offers the additional
architectural flexibility to achieve robust performance in handling some
degraded modalities, and show a significant performance improvement based on
Single Shot Detector (SSD) for KITTI dataset using the proposed fusion network
and data augmentation schemes.
