Weakly Supervised Incremental Segmentation via Semantic Anchors and Spatial Arbitration
The paper proposes SASA, a drift-resilient approach for weakly incremental learning in semantic segmentation. It uses learnable semantic anchors as rigid class-level references and elastic residual adaptation for stable yet flexible representation learning. A Spatial Label Arbitration mechanism filters noisy supervision and enforces the 'one object, one class' constraint. Experiments show state-of-the-art performance on standard benchmarks, especially in challenging multi-step incremental settings.
[2606.04060] Weakly Supervised Incremental Segmentation via Semantic Anchors and Spatial Arbitration
[Submitted on 2 Jun 2026]
Title:Weakly Supervised Incremental Segmentation via Semantic Anchors and Spatial Arbitration
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Abstract:Weakly Incremental Learning for Semantic Segmentation (WILSS) suffers from the continuous introduction of noisy supervision, which progressively corrupts class-level representations, leading to severe feature drift and semantic corruption, thereby causing newly learned classes to overwrite old ones. To address these issues, we propose a drift-resilient WILSS approach, named SASA, designed to stabilize semantic learning via Semantic Anchors and Spatial Arbitration. Specifically, at the representation level, we introduce semantic anchors of learnable tokens as rigid class-level references to preserve long-term semantic identity. Complementary to this, an elastic residual adaptation facilitates controlled, instance-specific refinement, ensuring a stable yet flexible learning trajectory. At the supervision level, we develop a Spatial Label Arbitration mechanism that performs geometry-aware decisions to directly filter unreliable signals and enforce a strict "one object, one class" constraint. By synergistically stabilizing representations and improving supervision reliability, SASA effectively mitigates feature drift under weak supervision. Extensive experiments on standard benchmarks demonstrate that our approach consistently outperforms existing state-of-the-art methods, particularly in challenging multi-step incremental settings. The code is available at this https URL.
Comments: Accepted by ICME2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.04060 [cs.CV]
(or arXiv:2606.04060v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.04060
arXiv-issued DOI via DataCite
Submission history
From: Kai Fang [view email] [v1] Tue, 2 Jun 2026 12:10:46 UTC (2,819 KB)
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