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Most of computer vision focuses on what is in an image. We propose to train astandalone object-centric context representation to perform the opposite task:seeing what is not there. Given an image, our context model can predict whereobjects should exist, even when no object instances are present. Combined withobject detection results, we can perform a novel vision task: finding whereobjects are missing in an image. Our model is based on a convolutional neuralnetwork structure. With a specially designed training strategy, the modellearns to ignore objects and focus on context only. It is fully convolutionalthus highly efficient. Experiments show the effectiveness of the proposedapproach in one important accessibility task: finding city street regions wherecurb ramps are missing, which could help millions of people with mobilitydisabilities.

Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing