  The scarcity of labeled data in real-world scenarios is a critical bottleneck
of deep learning's effectiveness. Semi-supervised semantic segmentation has
been a typical solution to achieve a desirable tradeoff between annotation cost
and segmentation performance. However, previous approaches, whether based on
consistency regularization or self-training, tend to neglect the contextual
knowledge embedded within inter-pixel relations. This negligence leads to
suboptimal performance and limited generalization. In this paper, we propose a
novel approach IPixMatch designed to mine the neglected but valuable
Inter-Pixel information for semi-supervised learning. Specifically, IPixMatch
is constructed as an extension of the standard teacher-student network,
incorporating additional loss terms to capture inter-pixel relations. It shines
in low-data regimes by efficiently leveraging the limited labeled data and
extracting maximum utility from the available unlabeled data. Furthermore,
IPixMatch can be integrated seamlessly into most teacher-student frameworks
without the need of model modification or adding additional components. Our
straightforward IPixMatch method demonstrates consistent performance
improvements across various benchmark datasets under different partitioning
protocols.
