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• W3021542222 abstract "Semi-supervised learning (SSL) has promising potential for improving the predictive performance of machine learning models using unlabeled data. There has been remarkable progress, but the scope of demonstration in SSL has been limited to image classification tasks. In this paper, we propose STAC, a simple yet effective SSL framework for visual object detection along with a data augmentation strategy. STAC deploys highly confident pseudo labels of localized objects from an unlabeled image and updates the model by enforcing consistency via strong augmentations. We propose new experimental protocols to evaluate performance of semi-supervised object detection using MS-COCO and demonstrate the efficacy of STAC on both MS-COCO and VOC07. On VOC07, STAC improves the AP$^{0.5}$ from 76.30 to 79.08; on MS-COCO, STAC demonstrates 2x higher data efficiency by achieving 24.38 mAP using only 5% labeled data than supervised baseline that marks 23.86% using 10% labeled data. The code is available at url{this https URL}." @default.
• W3021542222 created "2020-05-13" @default.
• W3021542222 creator A5017624817 @default.
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• W3021542222 date "2020-05-10" @default.
• W3021542222 modified "2023-09-26" @default.
• W3021542222 title "A Simple Semi-Supervised Learning Framework for Object Detection." @default.
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