Trace Back and Go Ahead: Completing partial annotation for continual semantic segmentation

Existing Continual Semantic Segmentation (CSS) methods effectively address the issue of background shift in regular training samples. However, this issue persists in exemplars, i.e. , replay samples, which is often overlooked. Each exemplar is annotated only with the classes from its originating task, while other past classes and the current classes during replay are labeled as background . This partial annotation can erase the network’s knowledge of previous classes and impede the learning of new classes. To resolve this, we introduce a new method named Trace Back and Go Ahead (TAGA), which utilizes a backward annotator model and a forward annotator model to generate pseudo-labels for both regular training samples and exemplars, aiming at reducing the adverse effects of incomplete annotations. This approach effectively mitigates the risk of incorrect guidance from both sample types, offering a comprehensive solution to background shift . Additionally, due to a significantly smaller number of exemplars compared to regular training samples, the class distribution in the sample pool of each incremental task exhibits a long-tailed pattern, potentially biasing classification towards incremental classes. Consequently, TAGA incorporates a class-equilibrium sampling strategy that adaptively adjusts the sampling frequencies based on the ratios of exemplars to regular samples and past to new classes, counteracting the skewed distribution. Extensive experiments on two public datasets, Pascal VOC 2012 and ADE20K, demonstrate that our method surpasses state-of-the-art methods. • Proposes a method to address background shift problems in all training samples. • Utilizes annotators to complete missing annotations for past and new classes. • Implements a class-equilibrium sampling strategy to long-tail challenges. • Demonstrates superior performance of TAGA over the state-of-the-art CSS methods.