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We propose a reinforcement learning based approach to query objectlocalization, for which an agent is trained to localize objects of interestspecified by a small exemplary set. We learn a transferable reward signalformulated using the exemplary set by ordinal metric learning. Our proposedmethod enables test-time policy adaptation to new environments where the rewardsignals are not readily available, and outperforms fine-tuning approaches thatare limited to annotated images. In addition, the transferable reward allowsrepurposing the trained agent from one specific class to another class.Experiments on corrupted MNIST, CU-Birds, and COCO datasets demonstrate theeffectiveness of our approach.

Learning Transferable Reward for Query Object Localization with Policy Adaptation