  Semi-supervised object detection (SSOD) aims to boost detection performance
by leveraging extra unlabeled data. The teacher-student framework has been
shown to be promising for SSOD, in which a teacher network generates
pseudo-labels for unlabeled data to assist the training of a student network.
Since the pseudo-labels are noisy, filtering the pseudo-labels is crucial to
exploit the potential of such framework. Unlike existing suboptimal methods, we
propose a two-step pseudo-label filtering for the classification and regression
heads in a teacher-student framework. For the classification head, OCL
(Object-wise Contrastive Learning) regularizes the object representation
learning that utilizes unlabeled data to improve pseudo-label filtering by
enhancing the discriminativeness of the classification score. This is designed
to pull together objects in the same class and push away objects from different
classes. For the regression head, we further propose RUPL
(Regression-Uncertainty-guided Pseudo-Labeling) to learn the aleatoric
uncertainty of object localization for label filtering. By jointly filtering
the pseudo-labels for the classification and regression heads, the student
network receives better guidance from the teacher network for object detection
task. Experimental results on Pascal VOC and MS-COCO datasets demonstrate the
superiority of our proposed method with competitive performance compared to
existing methods.
