Object Localization is the task of determining the spatial location of an object within an image. This typically involves providing a bounding box that specifies the object’s position and, in some cases, its orientation.

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Learning Transferable Reward for Query Object Localization with Policy Adaptation

Learning Transferable Reward for Query Object Localization with Policy Adaptation We propose a reinforcement learning based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal formulated using the exemplary set by ordinal metric learning. Our proposed method enables test-time policy adaptation to new environments where the reward signals are not readily available and outperforms fine-tuning approaches that are limited to annotated images. In addition, the transferable reward allows repurposing the trained agent from one specific class to another class. Experiments on corrupted MNIST, CU-Birds, and COCO datasets demonstrate the effectiveness of our approach.