提出无需存储旧数据的闭式解法，实现高效增量语义分割
CFSSeg: Closed-Form Solution for Class-Incremental Semantic Segmentation of 2D Images and 3D Point Clouds
- 用闭式解替代迭代优化，单次遍历新数据即可更新模型
- 在Pascal VOC2012、S3DIS和ScanNet上均超越现有方法
- 适合实时视频分析与隐私敏感场景的持续学习
2D图像与3D点云是多媒体应用的基础数据类型，涵盖实时视频分析、增强现实（AR）及3D场景理解。类增量语义分割（CSS）需在保留已有知识的同时逐步学习新类别。现有方法通常依赖基于随机梯度下降的耗时训练，采用复杂正则化或样本回放机制，但此类方法不可避免地更新历史权重，导致灾难性遗忘，尤其在像素/点级粒度下问题更严重。为此，本文提出CFSSeg，一种新颖的无样本回放方法，利用闭式解法，提供一种实用且理论严谨的持续语义分割解决方案。该方法无需迭代梯度优化，也无需存储过往数据，每步仅需对新样本进行一次遍历。不仅提升计算效率，还为动态、隐私敏感的多媒体环境提供可行方案。在Pascal VOC2012、S3DIS和ScanNet等2D与3D基准数据集上的大量实验表明，CFSSeg性能显著优于现有方法。
原文摘要 · Abstract (English)
2D images and 3D point clouds are foundational data types for multimedia applications, including real-time video analysis, augmented reality (AR), and 3D scene understanding. Class-incremental semantic segmentation (CSS) requires incrementally learning new semantic categories while retaining prior knowledge. Existing methods typically rely on computationally expensive training based on stochastic gradient descent, employing complex regularization or exemplar replay. However, stochastic gradient descent-based approaches inevitably update the model's weights for past knowledge, leading to catastrophic forgetting, a problem exacerbated by pixel/point-level granularity. To address these challenges, we propose CFSSeg, a novel exemplar-free approach that leverages a closed-form solution, offering a practical and theoretically grounded solution for continual semantic segmentation tasks. This eliminates the need for iterative gradient-based optimization and storage of past data, requiring only a single pass through new samples per step. It not only enhances computational efficiency but also provides a practical solution for dynamic, privacy-sensitive multimedia environments. Extensive experiments on 2D and 3D benchmark datasets such as Pascal VOC2012, S3DIS, and ScanNet demonstrate CFSSeg's superior performance.
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