A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection

• URL: http://arxiv.org/abs/2102.08884v1
• Date: Wed, 17 Feb 2021 17:27:21 GMT
• Title: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection
• Authors: Cheng Zhang, Tai-Yu Pan, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao
• Abstract summary: We take advantage of object-centric images to improve object detection in scene-centric images. We present a simple yet surprisingly effective framework to do so. Our approach can improve the object detection (and instance segmentation) accuracy of rare objects by 50% (and 33%) relatively.
• Score: 56.82077636126353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object frequencies in daily scenes follow a long-tailed distribution. Many objects do not appear frequently enough in scene-centric images (e.g., sightseeing, street views) for us to train accurate object detectors. In contrast, these objects are captured at a higher frequency in object-centric images, which are intended to picture the objects of interest. Motivated by this phenomenon, we propose to take advantage of the object-centric images to improve object detection in scene-centric images. We present a simple yet surprisingly effective framework to do so. On the one hand, our approach turns an object-centric image into a useful training example for object detection in scene-centric images by mitigating the domain gap between the two image sources in both the input and label space. On the other hand, our approach employs a multi-stage procedure to train the object detector, such that the detector learns the diverse object appearances from object-centric images while being tied to the application domain of scene-centric images. On the LVIS dataset, our approach can improve the object detection (and instance segmentation) accuracy of rare objects by 50% (and 33%) relatively, without sacrificing the performance of other classes.

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