  In this paper, we focus on semi-supervised object detection to boost
performance of proposal-based object detectors (a.k.a. two-stage object
detectors) by training on both labeled and unlabeled data. However, it is
non-trivial to train object detectors on unlabeled data due to the
unavailability of ground truth labels. To address this problem, we present a
proposal learning approach to learn proposal features and predictions from both
labeled and unlabeled data. The approach consists of a self-supervised proposal
learning module and a consistency-based proposal learning module. In the
self-supervised proposal learning module, we present a proposal location loss
and a contrastive loss to learn context-aware and noise-robust proposal
features respectively. In the consistency-based proposal learning module, we
apply consistency losses to both bounding box classification and regression
predictions of proposals to learn noise-robust proposal features and
predictions. Our approach enjoys the following benefits: 1) encouraging more
context information to delivered in the proposals learning procedure; 2) noisy
proposal features and enforcing consistency to allow noise-robust object
detection; 3) building a general and high-performance semi-supervised object
detection framework, which can be easily adapted to proposal-based object
detectors with different backbone architectures. Experiments are conducted on
the COCO dataset with all available labeled and unlabeled data. Results
demonstrate that our approach consistently improves the performance of
fully-supervised baselines. In particular, after combining with data
distillation, our approach improves AP by about 2.0% and 0.9% on average
compared to fully-supervised baselines and data distillation baselines
respectively.
