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.

通过理性诊断符号引导冲突，LogicDiag 逐步解决伪标签内的冲突，恢复错误伪标签，最终减轻错误积累问题；在三个标准半监督语义分割基准测试中证明了 LogicDiag 的有效性和广泛适用性，并突出了系统集成符号推理与统计、神经学习方法中出现的有希望的机会。