Learning Transferable Reward for Query Object Localization with Policy Adaptation

Publication Date: 4/25/2022

Event: Tenth International Conference on Learning Representations (ICLR 2022)

Reference: pp. 1-23, 2022

Authors: Tingfeng Li, NEC Laboratories America, Inc., Rutgers University; Shaobo Han, NEC Laboratories America, Inc.; Martin Renqiang Min, NEC Laboratories America, Inc.; Dimitris N. Metaxas, Rutgers University

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.

Publication Link: https://iclr.cc/virtual/2022/poster/6128