Learning Transferable Reward for Query Object Localization with Policy Adaptation
Tingfeng Li, Shaobo Han, Martin Renqiang Min, Dimitris N. Metaxas
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.
BibTeX
@inproceedings{
li2022learning,
title={Learning Transferable Reward for Query Object Localization with Policy Adaptation},
author={Tingfeng Li and Shaobo Han and Martin Renqiang Min and Dimitris N. Metaxas},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=92tYQiil17}
}