One-Shot General Object Localization

• URL: http://arxiv.org/abs/2211.13392v1
• Date: Thu, 24 Nov 2022 03:14:04 GMT
• Title: One-Shot General Object Localization
• Authors: Yang You, Zhuochen Miao, Kai Xiong, Weiming Wang, Cewu Lu
• Abstract summary: OneLoc is a general one-shot object localization algorithm. OneLoc efficiently finds the object center and bounding box size by a special voting scheme. Experiments show that the proposed method achieves state-of-the-art overall performance on two datasets.
• Score: 43.88712478006662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a general one-shot object localization algorithm called OneLoc. Current one-shot object localization or detection methods either rely on a slow exhaustive feature matching process or lack the ability to generalize to novel objects. In contrast, our proposed OneLoc algorithm efficiently finds the object center and bounding box size by a special voting scheme. To keep our method scale-invariant, only unit center offset directions and relative sizes are estimated. A novel dense equalized voting module is proposed to better locate small texture-less objects. Experiments show that the proposed method achieves state-of-the-art overall performance on two datasets: OnePose dataset and LINEMOD dataset. In addition, our method can also achieve one-shot multi-instance detection and non-rigid object localization. Code repository: https://github.com/qq456cvb/OneLoc.

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