Abstract
Self-supervised semantic segmentation methods often fail when faced with appearance ambiguities. We argue that this is due to an over-reliance on unstable, appearance-based features such as shadows, glare, and local textures. We propose \textbf{GASeg}, a novel framework that bridges appearance and geometry by leveraging stable topological information. The core of our method is Differentiable Box-Counting (\textbf{DBC}) module, which quantifies multi-scale topological statistics from two parallel streams: geometric-based features and appearance-based features. To force the model to learn these stable structural representations, we introduce Topological Augmentation (\textbf{TopoAug}), an adversarial strategy that simulates real-world ambiguities by applying morphological operators to the input images. A multi-objective loss, \textbf{GALoss}, then explicitly enforces cross-modal alignment between geometric-based and appearance-based features. Extensive experiments demonstrate that GASeg achieves state-of-the-art performance on four benchmarks, including COCO-Stuff, Cityscapes, and PASCAL, validating our approach of bridging geometry and appearance via topological information.
Abstract (translated)
自监督语义分割方法在面对外观模糊性时往往表现不佳。我们认为这是由于过度依赖诸如阴影、眩光和局部纹理等不稳定的外观特征所致。为此，我们提出了\textbf{GASeg}这一新颖框架，它通过利用稳定拓扑信息来弥合外观与几何之间的差距。我们的方法核心是可微分盒计数（\textbf{DBC}）模块，该模块从两个并行流中量化多尺度的拓扑统计：基于几何特征和基于外观特征。 为了促使模型学习这些稳定的结构表示，我们引入了拓扑增强（\textbf{TopoAug}），这是一种对抗策略，通过将形态学算子应用于输入图像来模拟现实世界的模糊性。随后，一个多目标损失函数 \textbf{GALoss} 明确地强制执行基于几何特征和外观特征之间的跨模态对齐。 大量的实验表明，GASeg 在包括 COCO-Stuff、Cityscapes 和 PASCAL 在内的四个基准数据集上达到了最先进的性能水平，验证了我们通过拓扑信息来连接几何与外观的方法的有效性。
URL
https://arxiv.org/abs/2512.23997
