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    LEARNING ORDINAL REPRESENTATIONS FOR DEEP REINFORCEMENT LEARNING BASED OBJECT LOCALIZATION
    1.
    发明申请
    LEARNING ORDINAL REPRESENTATIONS FOR DEEP REINFORCEMENT LEARNING BASED OBJECT LOCALIZATION 审中-公开

    公开(公告)号：WO2022217122A1

    公开(公告)日：2022-10-13

    申请号：PCT/US2022/024118

    申请日：2022-04-08

    Applicant: NEC LABORATORIES AMERICA, INC.

    Inventor： HAN, Shaobo ,  MIN, Renqiang ,  LI, Tingfeng

    IPC: G06V10/82 ,  G06V10/25 ,  G06N3/08 ,  G06N3/04

    Abstract: A reinforcement learning based approach to the problem of query object localization, where 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. It enables test-time policy adaptation to new environments where the reward signals are not readily available, and thus outperforms fine-tuning approaches that are limited to annotated images. In addition, the transferable reward allows repurposing of the trained agent for new tasks, such as annotation refinement, or selective localization from multiple common objects across a set of images. Experiments on corrupted MNIST dataset and CU-Birds dataset demonstrate the effectiveness of our approach.

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