  Occlusion removal is an interesting application of image enhancement, for
which, existing work suggests manually-annotated or domain-specific occlusion
removal. No work tries to address automatic occlusion detection and removal as
a context-aware generic problem. In this paper, we present a novel methodology
to identify objects that do not relate to the image context as occlusions and
remove them, reconstructing the space occupied coherently. The proposed system
detects occlusions by considering the relation between foreground and
background object classes represented as vector embeddings, and removes them
through inpainting. We test our system on COCO-Stuff dataset and conduct a user
study to establish a baseline in context-aware automatic occlusion removal.
