GASeg: Robust Self-Supervised Segmentation with Topology
Research Paper#Computer Vision, Semantic Segmentation, Self-Supervised Learning, Topology🔬 Research|Analyzed: Jan 3, 2026 18:21•
Published: Dec 30, 2025 05:34
•1 min read
•ArXivAnalysis
This paper addresses the limitations of self-supervised semantic segmentation methods, particularly their sensitivity to appearance ambiguities. It proposes a novel framework, GASeg, that leverages topological information to bridge the gap between appearance and geometry. The core innovation is the Differentiable Box-Counting (DBC) module, which extracts multi-scale topological statistics. The paper also introduces Topological Augmentation (TopoAug) to improve robustness and a multi-objective loss (GALoss) for cross-modal alignment. The focus on stable structural representations and the use of topological features is a significant contribution to the field.
Key Takeaways
- •Proposes GASeg, a novel framework for self-supervised semantic segmentation.
- •Leverages topological information to overcome appearance ambiguities.
- •Introduces Differentiable Box-Counting (DBC) for multi-scale topological statistics.
- •Employs Topological Augmentation (TopoAug) for robustness.
- •Uses a multi-objective loss (GALoss) for cross-modal alignment.
- •Achieves state-of-the-art performance on multiple benchmarks.
Reference / Citation
View Original"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."