  This paper focuses on Semi-Supervised Object Detection (SSOD). Knowledge
Distillation (KD) has been widely used for semi-supervised image
classification. However, adapting these methods for SSOD has the following
obstacles. (1) The teacher model serves a dual role as a teacher and a student,
such that the teacher predictions on unlabeled images may be very close to
those of student, which limits the upper-bound of the student. (2) The class
imbalance issue in SSOD hinders an efficient knowledge transfer from teacher to
student. To address these problems, we propose a novel method Temporal
Self-Ensembling Teacher (TSE-T) for SSOD. Differently from previous KD based
methods, we devise a temporally evolved teacher model. First, our teacher model
ensembles its temporal predictions for unlabeled images under stochastic
perturbations. Second, our teacher model ensembles its temporal model weights
with the student model weights by an exponential moving average (EMA) which
allows the teacher gradually learn from the student. These self-ensembling
strategies increase data and model diversity, thus improving teacher
predictions on unlabeled images. Finally, we use focal loss to formulate
consistency regularization term to handle the data imbalance problem, which is
a more efficient manner to utilize the useful information from unlabeled images
than a simple hard-thresholding method which solely preserves confident
predictions. Evaluated on the widely used VOC and COCO benchmarks, the mAP of
our method has achieved 80.73% and 40.52% on the VOC2007 test set and the
COCO2014 minval5k set respectively, which outperforms a strong fully-supervised
detector by 2.37% and 1.49%. Furthermore, our method sets the new
state-of-the-art in SSOD on VOC2007 test set which outperforms the baseline
SSOD method by 1.44%. The source code of this work is publicly available at
http://github.com/syangdong/tse-t.
