基于策略自适应的查询对象定位可转移奖励学习算法
Learning Transferable Reward for Query Object Localization with Policy Adaptation
摘要：
我们提出了一种基于强化学习的方法来查询对象定位，其中代理被训练来定位由一个小的示例集指定的感兴趣的对象。我们学习一个可转移的奖励信号制定使用的示范性的顺序度量学习。我们提出的方法使测试时的政策适应新的环境中的奖励信号是不容易获得的，并优于微调方法，仅限于注释的图像。此外，可转移的奖励允许将经过训练的代理从一个特定类别重新用于另一个类别。在MNIST、CU-Birds和COCO数据集上的实验证明了该方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
