Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection

• URL: http://arxiv.org/abs/2306.02268v1
• Date: Sun, 4 Jun 2023 06:01:53 GMT
• Title: Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection
• Authors: Purbayan Kar, Vishal Chudasama, Naoyuki Onoe, Pankaj Wasnik
• Abstract summary: Semi-supervised object detection (SSOD) has made significant progress with the development of pseudo-label-based end-to-end methods. Many methods face challenges due to class imbalance, which hinders the effectiveness of the pseudo-label generator. In this paper, we examine the root causes of low-quality pseudo-labels and present novel learning mechanisms to improve the label generation quality.
• Score: 1.6249267147413524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised object detection (SSOD) has made significant progress with the development of pseudo-label-based end-to-end methods. However, many of these methods face challenges due to class imbalance, which hinders the effectiveness of the pseudo-label generator. Furthermore, in the literature, it has been observed that low-quality pseudo-labels severely limit the performance of SSOD. In this paper, we examine the root causes of low-quality pseudo-labels and present novel learning mechanisms to improve the label generation quality. To cope with high false-negative and low precision rates, we introduce an adaptive thresholding mechanism that helps the proposed network to filter out optimal bounding boxes. We further introduce a Jitter-Bagging module to provide accurate information on localization to help refine the bounding boxes. Additionally, two new losses are introduced using the background and foreground scores predicted by the teacher and student networks to improvise the pseudo-label recall rate. Furthermore, our method applies strict supervision to the teacher network by feeding strong & weak augmented data to generate robust pseudo-labels so that it can detect small and complex objects. Finally, the extensive experiments show that the proposed network outperforms state-of-the-art methods on MS-COCO and Pascal VOC datasets and allows the baseline network to achieve 100% supervised performance with much less (i.e., 20%) labeled data.

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