Calibrated Teacher for Sparsely Annotated Object Detection

• URL: http://arxiv.org/abs/2303.07582v1
• Date: Tue, 14 Mar 2023 02:02:39 GMT
• Title: Calibrated Teacher for Sparsely Annotated Object Detection
• Authors: Haohan Wang, Liang Liu, Boshen Zhang, Jiangning Zhang, Wuhao Zhang, Zhenye Gan, Yabiao Wang, Chengjie Wang, Haoqian Wang
• Abstract summary: Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidable missing annotations. Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations. We propose a Calibrated Teacher, of which the confidence estimation of the prediction is well to match its real precision.
• Score: 35.74904852812749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidable missing annotations. As a result, the incomplete annotation in each image could provide misleading supervision and harm the training. Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations. Such a mechanism is sensitive to the threshold of the pseudo label score. However, the effective threshold is different in different training stages and among different object detectors. Therefore, the current methods with fixed thresholds have sub-optimal performance, and are difficult to be applied to other detectors. In order to resolve this obstacle, we propose a Calibrated Teacher, of which the confidence estimation of the prediction is well calibrated to match its real precision. In this way, different detectors in different training stages would share a similar distribution of the output confidence, so that multiple detectors could share the same fixed threshold and achieve better performance. Furthermore, we present a simple but effective Focal IoU Weight (FIoU) for the classification loss. FIoU aims at reducing the loss weight of false negative samples caused by the missing annotation, and thus works as the complement of the teacher-student paradigm. Extensive experiments show that our methods set new state-of-the-art under all different sparse settings in COCO. Code will be available at https://github.com/Whileherham/CalibratedTeacher.

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