  Current deep learning methods for object recognition are purely data-driven
and require a large number of training samples to achieve good results. Due to
their sole dependence on image data, these methods tend to fail when confronted
with new environments where even small deviations occur. Human perception,
however, has proven to be significantly more robust to such distribution
shifts. It is assumed that their ability to deal with unknown scenarios is
based on extensive incorporation of contextual knowledge. Context can be based
either on object co-occurrences in a scene or on memory of experience. In
accordance with the human visual cortex which uses context to form different
object representations for a seen image, we propose an approach that enhances
deep learning methods by using external contextual knowledge encoded in a
knowledge graph. Therefore, we extract different contextual views from a
generic knowledge graph, transform the views into vector space and infuse it
into a DNN. We conduct a series of experiments to investigate the impact of
different contextual views on the learned object representations for the same
image dataset. The experimental results provide evidence that the contextual
views influence the image representations in the DNN differently and therefore
lead to different predictions for the same images. We also show that context
helps to strengthen the robustness of object recognition models for
out-of-distribution images, usually occurring in transfer learning tasks or
real-world scenarios.
