# Learning Transferable Reward for Query Object Localization with Policy Adaptation Source: [Learning Transferable Reward for Query Object Localization with Policy Adaptation]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:13:32.751Z Authors: Tingfeng Li, Shaobo Han, Martin Renqiang Min, Dimitris N. Metaxas Published: 2022-02-24T22:52:14.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()