  Recent advances in semi-supervised semantic segmentation have been heavily
reliant on pseudo labeling to compensate for limited labeled data, disregarding
the valuable relational knowledge among semantic concepts. To bridge this gap,
we devise LogicDiag, a brand new neural-logic semi-supervised learning
framework. Our key insight is that conflicts within pseudo labels, identified
through symbolic knowledge, can serve as strong yet commonly ignored learning
signals. LogicDiag resolves such conflicts via reasoning with logic-induced
diagnoses, enabling the recovery of (potentially) erroneous pseudo labels,
ultimately alleviating the notorious error accumulation problem. We showcase
the practical application of LogicDiag in the data-hungry segmentation
scenario, where we formalize the structured abstraction of semantic concepts as
a set of logic rules. Extensive experiments on three standard semi-supervised
semantic segmentation benchmarks demonstrate the effectiveness and generality
of LogicDiag. Moreover, LogicDiag highlights the promising opportunities
arising from the systematic integration of symbolic reasoning into the
prevalent statistical, neural learning approaches.
