  In this paper, we study the impact of motion blur, a common quality flaw in
real world images, on a state-of-the-art two-stage image captioning solution,
and notice a degradation in solution performance as blur intensity increases.
We investigate techniques to improve the robustness of the solution to motion
blur using training data augmentation at each or both stages of the solution,
i.e., object detection and captioning, and observe improved results. In
particular, augmenting both the stages reduces the CIDEr-D degradation for high
motion blur intensity from 68.7 to 11.7 on MS COCO dataset, and from 22.4 to
6.8 on Vizwiz dataset.
