FuTCR: 未来目标对比与排斥用于持续全景分割
FuTCR: Future-Targeted Contrast and Repulsion for Continual Panoptic Segmentation
AI总结：
本文提出FuTCR框架，通过重构表示来解决持续全景分割中区分新增背景类别的挑战，通过像素到区域对比和排斥机制提升新类别性能，实验显示在六个CPS设置和多种数据集规模下，FuTCR在相对新类别全景质量上比最先进的方法提升高达28%，同时保持或提升基础类性能。
摘要：
持续全景分割（CPS）需要能够快速适应新类别的方法。由于该密集预测任务的性质，训练图像可能包含标记和未标记的对象。由于对这些未标记对象一无所知，现有方法通常在训练时将任何未标记像素归为一个'背景'类别。实际上，在训练过程中，它们反复告诉模型所有不同的背景类别都是相同的（即使它们不是）。这使得学习区分随着新增的背景类别而变得具有挑战性，因为这些新类别可能需要使用模型之前被告诉不重要并被忽略的信息。因此，我们提出了一种面向未来的对比和排斥（FuTCR）框架，通过在新类别引入之前重构表示来解决这一限制。FuTCR首先通过将模型预测的掩码中像素始终被分类为背景但表现出非背景logits的区域进行分组，发现自信的未来样区域。接着，FuTCR通过像素到区域对比从这些未标记区域构建连贯的原型，同时同时排斥背景特征远离已知类原型，以显式保留代表空间给未来类别。在六个CPS设置和多种数据集规模的实验中，FuTCR在相对新类别全景质量上比最先进的方法提升高达28%，同时保持或提升基础类性能，提升幅度高达4%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
