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Supervised learning based object detection frameworks demand plenty oflaborious manual annotations, which may not be practical in real applications.Semi-supervised object detection (SSOD) can effectively leverage unlabeled datato improve the model performance, which is of great significance for theapplication of object detection models. In this paper, we revisit SSOD andpropose Instant-Teaching, a completely end-to-end and effective SSOD framework,which uses instant pseudo labeling with extended weak-strong data augmentationsfor teaching during each training iteration. To alleviate the confirmation biasproblem and improve the quality of pseudo annotations, we further propose aco-rectify scheme based on Instant-Teaching, denoted as Instant-Teaching$^*$.Extensive experiments on both MS-COCO and PASCAL VOC datasets substantiate thesuperiority of our framework. Specifically, our method surpassesstate-of-the-art methods by 4.2 mAP on MS-COCO when using $2\%$ labeled data.Even with full supervised information of MS-COCO, the proposed method stilloutperforms state-of-the-art methods by about 1.0 mAP. On PASCAL VOC, we canachieve more than 5 mAP improvement by applying VOC07 as labeled data and VOC12as unlabeled data.

Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework