Related papers: Learning Transferable Reward for Query Object Localization with Policy Adaptation

Learning Transferable Reward for Query Object Localization with Policy Adaptation

URL: http://arxiv.org/abs/2202.12403v1
Date: Thu, 24 Feb 2022 22:52:14 GMT
Title: Learning Transferable Reward for Query Object Localization with Policy Adaptation
Authors: Tingfeng Li, Shaobo Han, Martin Renqiang Min, Dimitris N. Metaxas
Abstract summary: 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.
Score: 49.994989590997655
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We propose a reinforcement learning based approach to \emph{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.

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