Continual Panoptic Segmentation (CPS) requires methods that can quickly adapt to new categories over time. The nature of this dense prediction task means that training images may contain a mix of labeled and unlabeled objects. As nothing is known about these unlabeled objects a priori, existing methods often simply group any unlabeled pixel into a single "background" class during training. In effect, during training, they repeatedly tell the model that all the different background categories are the same (even when they aren't). This makes learning to identify different background categories as they are added challenging since these new categories may require using information the model was previously told was unimportant and ignored. Thus, we propose a Future-Targeted Contrastive and Repulsive (FuTCR) framework that addresses this limitation by restructuring representations before new classes are introduced. FuTCR first discovers confident future-like regions by grouping model-predicted masks whose pixels are consistently classified as background but exhibit non-background logits. Next, FuTCR applies pixel-to-region contrast to build coherent prototypes from these unlabeled regions, while simultaneously repelling background features away from known-class prototypes to explicitly reserve representational space for future categories. Experiments across six CPS settings and a range of dataset sizes show FuTCR improves relative new-class panoptic quality over the state-of-the-art by up to 28%, while preserving or improving base-class performance with gains up to 4%.

翻译：连续全景分割（CPS）要求方法能随时间快速适应新类别。由于该密集预测任务的性质，训练图像可能同时包含已标注和未标注对象。鉴于这些未标注对象先验未知，现有方法通常简单地将所有未标注像素归类为训练过程中的单一"背景"类别。实际上，这等于反复告诉模型所有不同背景类别都是相同的（即便它们实际不同）。当新类别被引入时，这种处理方式会使得识别不同背景类别变得困难，因为这些新类别可能需要利用模型先前被判定为无关且被忽略的信息。为此，我们提出面向未来目标的对比与排斥（FuTCR）框架，通过在新类别引入前重构特征表示来突破这一局限。FuTCR首先通过分组模型预测的掩码发现可信的未来类区域——这些掩码的像素虽被持续归类为背景，但其logit值呈现非背景特征。接着，FuTCR应用像素到区域的对比学习构建这些未标注区域的连贯原型，同时将背景特征从已知类别原型中排斥开来，为未来类别显式预留表示空间。在六种CPS设置及不同规模数据集上的实验表明，FuTCR相较于现有最优方法，新类别的全景分割质量相对提升最高达28%，同时保持或提升基类性能（增益最高达4%）。