  Due to the costliness of labelled data in real-world applications,
semi-supervised object detectors, underpinned by pseudo labelling, are
appealing. However, handling confusing samples is nontrivial: discarding
valuable confusing samples would compromise the model generalisation while
using them for training would exacerbate the confirmation bias issue caused by
inevitable mislabelling. To solve this problem, this paper proposes to use
confusing samples proactively without label correction. Specifically, a virtual
category (VC) is assigned to each confusing sample such that they can safely
contribute to the model optimisation even without a concrete label. It is
attributed to specifying the embedding distance between the training sample and
the virtual category as the lower bound of the inter-class distance. Moreover,
we also modify the localisation loss to allow high-quality boundaries for
location regression. Extensive experiments demonstrate that the proposed VC
learning significantly surpasses the state-of-the-art, especially with small
amounts of available labels.
