Adversarially Optimizing Intersection over Union for Object Localization Tasks.
An implicit uncertainty exists in the annotations of computer visions datasets due to annotator disagreement and the high-dimensional space that annotations must be selected from.Rather than attempting to remove all annotation uncertainty,which we view as hopeless, or ignoring it, which can be detrimental, we choose to embrace uncertainty in the design of our learning approach. Specifically, we address uncertainty adversarially by approximating provided datasets annotations within a game-theoretic formulation of prediction tasks. The adversarial approximator is constrained to resemble the training data annotations according to a set of specified features.This induces a learned feature-based potential function that we then apply to new test cases. We demonstrate the efficiency and predictive performance of our approach on the ILSVRC2012 image dataset, showing significant improvements over existing methods.
Publisher URL: http://arxiv.org/abs/1710.07735
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