FuTCR: Future-Targeted Contrast and Repulsion for Continual Panoptic Segmentation

TL;DR

FuTCR introduces a novel framework for continual panoptic segmentation that enhances the model's ability to distinguish and reserve space for new categories by restructuring representations before they are introduced.

Contribution

The paper proposes FuTCR, a framework that discovers future-like regions and applies contrastive and repulsive learning to improve continual panoptic segmentation.

Findings

FuTCR improves new-class panoptic quality by up to 28%.

It preserves or enhances base-class performance with gains up to 4%.

Experiments across six CPS settings demonstrate its effectiveness.

Abstract

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%.