Continual Semantic Segmentation (CSS) suffers from catastrophic forgetting, particularly challenging for traditional per-pixel methods. Our prior work, CoMasTRe (CVPR 2024), introduced a query-based approach leveraging objectness by disentangling CSS into objectness learning and class recognition stages. While effective, CoMasTRe exhibited performance limitations due to feature forgetting within its pixel decoder. This paper presents CoMasTRe+, an enhanced framework specifically designed to overcome this limitation. The core contribution is a novel plugin, the Mixture of Continual Adapters (MoCA), integrated into the pixel decoder. MoCA is a dynamic architecture that mitigates feature forgetting by learning task-specific expert adapters. Crucially, MoCA employs a task-aware routing strategy and a novel adaptive routing distillation objective, tailored for continual learning, to preserve specialized feature representations across sequential tasks. CoMasTRe+ further enhances the class decoder using MoCA for improved recognition and simplicity. We extensively evaluate CoMasTRe+ on PASCAL VOC and ADE20K for continual semantic and panoptic segmentation. Experiments demonstrate that CoMasTRe+ effectively addresses the identified feature forgetting issue, significantly outperforms the original CoMasTRe, and achieves state-of-the-art results compared to both per-pixel and query-based baselines.

Generalist Anomaly Detection (GAD) aims to train a unified model on an original domain that can detect anomalies in new target domains. Previous GAD methods primarily use only normal samples as references, overlooking the valuable information contained in anomalous samples that are often available in real-world scenarios. To address this limitation, we propose a more practical approach: normal-abnormal-guided generalist anomaly detection, which leverages both normal and anomalous samples as references to guide anomaly detection across diverse domains. We introduce the Normal-Abnormal Generalist Learning (NAGL) framework, consisting of two key components: Residual Mining (RM) and Anomaly Feature Learning (AFL). RM extracts abnormal patterns from normal-abnormal reference residuals to establish transferable anomaly representations, while AFL adaptively learns anomaly features in query images through residual mapping to identify instance-aware anomalies. Our approach effectively utilizes both normal and anomalous references for more accurate and efficient cross-domain anomaly detection. Extensive experiments across multiple benchmarks demonstrate that our method significantly outperforms existing GAD approaches. This work represents the first to adopt a mixture of normal and abnormal samples as references in generalist anomaly detection. The code and datasets are available at https://github.com/JasonKyng/NAGL.

Most continual segmentation methods tackle the prob-lem as a per-pixel classification task. However, such a paradigm is very challenging, and we find query-based seg-menters with built-in objectness have inherent advantages compared with per-pixel ones, as objectness has strong transfer ability and forgetting resistance. Based on these findings, we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classi-fication. CoMasTRe uses a two-stage segmenter learning class-agnostic mask proposals at the first stage and leaving recognition to the second stage. During continual learning, a simple but effective distillation is adopted to strengthen objectness. To further mitigate the forgetting of old classes, we design a multi-label class distillation strategy suited for segmentation. We assess the effectiveness of CoMas-TRe on PASCAL VOC and ADE20K. Extensive experiments show that our method outperforms per-pixel and query-based methods on both datasets. Code will be available at https://github.com/jordangong/CoMasTRe.