Beyond Weakly Supervised: Pseudo Ground Truths Mining for Missing Bounding-Boxes Object Detection

Due to the shortcomings of the weakly supervised and fully supervised object detection (i.e., unsatisfactory performance and expensive annotations, respectively), leveraging partially labeled images in a cost-effective way to train an object detector has attracted much attention. In this paper, we formulate this challenging task as a missing bounding-boxes' object detection problem. Specifically, we develop a pseudo ground truth mining procedure to automatically find the missing bounding boxes for the unlabeled instances, called pseudo ground truths here, in the training data, and then combine the mined pseudo ground truths and the labeled annotations to train a fully supervised object detector. Furthermore, we propose an incremental learning framework to gradually incorporate the results of the trained fully supervised detector to improve the performance of the missing bounding-boxes' object detection. More importantly, we find an effective way to label the massive images with limited labors and funds, which is crucial when building a large-scale weakly/webly labeled dataset for object detection. The extensive experiments on the PASCAL VOC and COCO benchmarks demonstrate that our proposed method can narrow the gap between the fully supervised and weakly supervised object detectors, and outperform the previous state-of-the-art weakly supervised detectors by a large margin (more than 3% mAP absolutely) when the missing rate equals 0.9. Moreover, our proposed method with 30% missing bounding-box annotations can achieve comparable performance to some fully supervised detectors.