Learning Transferable Reward for Query Object Localization with Policy Adaptation
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.
Cite
Text
Li et al. "Learning Transferable Reward for Query Object Localization with Policy Adaptation." International Conference on Learning Representations, 2022.Markdown
[Li et al. "Learning Transferable Reward for Query Object Localization with Policy Adaptation." International Conference on Learning Representations, 2022.](https://mlanthology.org/iclr/2022/li2022iclr-learning/)BibTeX
@inproceedings{li2022iclr-learning,
title = {{Learning Transferable Reward for Query Object Localization with Policy Adaptation}},
author = {Li, Tingfeng and Han, Shaobo and Min, Martin Renqiang and Metaxas, Dimitris N.},
booktitle = {International Conference on Learning Representations},
year = {2022},
url = {https://mlanthology.org/iclr/2022/li2022iclr-learning/}
}