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.

翻译：近年来，半监督语义分割的进展高度依赖伪标签来弥补标注数据不足，却忽略了语义概念间有价值的关系知识。为弥合这一差距，我们提出LogicDiag——一种全新的神经逻辑半监督学习框架。我们的核心见解是：通过符号知识识别的伪标签内部冲突，可以作为强大但常被忽视的学习信号。LogicDiag通过逻辑诱导的诊断进行推理来解决此类冲突，从而恢复（潜在的）错误伪标签，最终缓解臭名昭著的误差累积问题。我们在数据饥渴的分割场景中展示了LogicDiag的实际应用，将语义概念的结构化抽象形式化为一组逻辑规则。在三个标准半监督语义分割基准上的大量实验证明了LogicDiag的有效性和通用性。此外，LogicDiag凸显了将符号推理系统性整合到主流的统计性、神经学习方法中所带来的有前景的机遇。