A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

How do we learn an object detector that is invariant to occlusions and deformations? Our current solution is to use a data-driven strategy - collect large-scale datasets which have object instances under different conditions. The hope is that the final classifier can use these examples to learn invariances. But is it really possible to see all the occlusions in a dataset? We argue that like categories, occlusions and object deformations also follow a long-tail. Some occlusions and deformations are so rare that they hardly happen, yet we want to learn a model invariant to such occurrences. In this paper, we propose an alternative solution. We propose to learn an adversarial network that generates examples with…

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