  Due to the costliness of labelled data in real-world applications,
semi-supervised learning, underpinned by pseudo labelling, is an appealing
solution. However, handling confusing samples is nontrivial: discarding
valuable confusing samples would compromise the model generalisation while
using them for training would exacerbate the issue of confirmation bias caused
by the resulting 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 in
such a way that it can safely contribute to the model optimisation even without
a concrete label. This provides an upper bound for inter-class information
sharing capacity, which eventually leads to a better embedding space. Extensive
experiments on two mainstream dense prediction tasks -- semantic segmentation
and object detection, demonstrate that the proposed VC learning significantly
surpasses the state-of-the-art, especially when only very few labels are
available. Our intriguing findings highlight the usage of VC learning in dense
vision tasks.
