Co-Student: Collaborating Strong and Weak Students for Sparsely Annotated Object Detection

Lianjun Wu, Jiangxiao Han, Zengqiang Zheng, Xinggang Wang* ;

"Sparsely Annotated Object Detection (SAOD) tackles the issue of incomplete labeling in object detection. Compared with Fully Annotated Object Detection (FAOD), SAOD is more complicated and challenging. Unlabeled objects tend to provide wrong supervision to the detectors during training, resulting in inferior performance for prevalent object detectors. Shrinking the performance gap between SAOD and FAOD does contribute to reducing the labeling cost. Existing methods tend to exploit pseudo-labeling for unlabeled objects while suffering from two issues: (1) they fail to make full use of unlabeled objects mined from the student detector and (2) the pseudo-labels contain much noise. To tackle those two issues, we introduce , a novel framework aiming to bridge the gap between SAOD and FAOD via fully exploiting the pseudo-labels from both teacher and student detectors. The proposed comprises a sophisticated teacher to denoise the pseudo-labels for unlabeled objects and two collaborative students that leverage strong and weak augmentations to excavate pseudo-labels. The students exchange the denoised pseudo-labels and learn from each other with consistency regularization brought by strong-weak augmentations. Without bells and whistles, the proposed framework with the one-stage detector, , FCOS, can achieve state-of-the-art performance on the COCO dataset with sparse annotations under diverse settings. Compared to previous works, it obtains 1.0%∼3.0% AP improvements under five settings of sparse annotations and achieves 95.1% performance compared to FCOS trained on fully annotated COCO dataset. Code has been made available at https://github.com/hustvl/ CoStudent."