Weakly Supervised Incremental Segmentation via Semantic Anchors and Spatial Arbitration

• URL: http://arxiv.org/abs/2606.04060v1
• Date: Tue, 02 Jun 2026 12:10:46 GMT
• Title: Weakly Supervised Incremental Segmentation via Semantic Anchors and Spatial Arbitration
• Abstract summary: SASA is designed to stabilize semantic learning via Semantic Anchors and Spatial Arbitration.<n>At the representation level, we introduce semantic anchors of learnable tokens as rigid class-level references to preserve long-term semantic identity.<n>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.
• Score: 8.498917049783191
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/ZhonggaiWang/SASA.

Related papers

• Unsupervised Point Cloud Registration via Training-Time Semantic Guidance [46.92635414824713]
  CAESAR is a teacher-student framework guided by an off-the-shelf 3D segmentation model exclusively during training.<n>Building on this, a train-only Semantic-Geometric Label Mining performs lightweight, batch-specific teacher refinement and mines reliable pseudo-labels.<n>Experiments on KITTI and nuScenes demonstrate state-of-the-art performance, with pronounced gains on the challenging nuScenes benchmark.
  arXiv  Detail & Related papers  (2026-09-14T08:49:09Z)
• AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE [51.994752158953084]
  AnchorMoE is an interpretable-by-construction classification framework.<n>It encodes multi-view representations of local patches and routes them to specialized experts.<n>Experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance.
  arXiv  Detail & Related papers  (2026-06-02T13:30:54Z)
• Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation [34.401271440453044]
  Learning to Label casts pseudo-label construction as a learnable decision-making process.<n>To ensure stable learning, reinforced pseudo-label selection is formulated as an exploratory decision process.<n>Experiments on RefCOCO, RefCO+, and RefCOCOg demonstrate improvements over existing methods.
  arXiv  Detail & Related papers  (2026-05-27T09:54:26Z)
• Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging [65.11234199720428]
  Continual Test-Time Adaptation aims to empower perception systems to handle dynamic distribution shifts encountered after deployment.<n>We introduce a novel forward-facilitation paradigm through a method termed Dynamic Style Bridging.<n>During test time, to mitigate inherent generative bias and adapt these proxies to incoming data, we propose a multi-level bridging mechanism.
  arXiv  Detail & Related papers  (2026-05-18T16:18:17Z)
• AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection [23.80607349547519]
  We propose AIFIND, Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection.<n>We use semantic anchors to stabilize incremental learning.
  arXiv  Detail & Related papers  (2026-04-17T16:17:12Z)
• CORE: Context-Robust Remasking for Diffusion Language Models [51.59514489363897]
  We propose Context-Robust Remasking (CORE), a training-free framework for inference-time revision.<n>Rather than trusting static token probabilities, CORE identifies context-brittle tokens by probing their sensitivity to targeted masked-context perturbations.<n>On LLaDA-8B-Base, CORE delivers consistent improvements across reasoning and code benchmarks, outperforming compute-matched baselines and improving MBPP by up to 9.2 percentage points.
  arXiv  Detail & Related papers  (2026-02-04T00:12:30Z)
• Unsupervised Online 3D Instance Segmentation with Synthetic Sequences and Dynamic Loss [52.28880405119483]
  Unsupervised online 3D instance segmentation is a fundamental yet challenging task.<n>Existing methods, such as UNIT, have made progress in this direction but remain constrained by limited training diversity.<n>We propose a new framework that enriches the training distribution through synthetic point cloud sequence generation.
  arXiv  Detail & Related papers  (2025-09-27T08:53:27Z)
• LLM-Assisted Semantic Guidance for Sparsely Annotated Remote Sensing Object Detection [25.9348571356454]
  LLM-assisted semantic guidance framework tailored for sparsely annotated remote sensing object detection.<n>Dense Pseudo-Label Assignment mechanism adaptively assigns pseudo-labels for both unlabeled and sparsely labeled data.<n> Adaptive Hard-Negative Reweighting Module to stabilize the supervised learning branch.
  arXiv  Detail & Related papers  (2025-09-21T08:05:43Z)
• Uncertainty-Participation Context Consistency Learning for Semi-supervised Semantic Segmentation [9.546065701435532]
  Semi-supervised semantic segmentation has attracted considerable attention for its ability to mitigate the reliance on extensive labeled data.<n>This paper proposes the Uncertainty-participation Context Consistency Learning (UCCL) method to explore richer supervisory signals.
  arXiv  Detail & Related papers  (2024-12-23T06:49:59Z)
• Source-Free Domain Adaptive Object Detection with Semantics Compensation [54.00183496587841]
  We introduce Weak-to-strong Semantics Compensation (WSCo) for strong data augmentation.<n>WSCo compensates for the class-relevant semantics that may be lost during strong augmentation on the fly.<n>WSCo can be implemented as a generic plug-in, easily integrable with any existing SFOD pipelines.
  arXiv  Detail & Related papers  (2024-10-07T23:32:06Z)
• Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization [77.62516752323207]
  We introduce an orthogonal fine-tuning method for efficiently fine-tuning pretrained weights and enabling enhanced robustness and generalization. A self-regularization strategy is further exploited to maintain the stability in terms of zero-shot generalization of VLMs, dubbed OrthSR. For the first time, we revisit the CLIP and CoOp with our method to effectively improve the model on few-shot image classficiation scenario.
  arXiv  Detail & Related papers  (2024-07-11T10:35:53Z)
• Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation [91.83820250747935]
  Pseudo-label noise is mainly contained in unstable samples in which predictions of most pixels undergo significant variations during self-training. We introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples. SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings.
  arXiv  Detail & Related papers  (2024-06-10T21:44:52Z)
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection [37.99031842449251]
  Video anomaly detection under weak supervision presents significant challenges. We present a weakly supervised anomaly detection framework that focuses on efficient context modeling and enhanced semantic discriminability. Our approach significantly improves the detection accuracy of certain anomaly sub-classes, underscoring its practical value and efficacy.
  arXiv  Detail & Related papers  (2023-06-26T06:45:16Z)
• Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation [63.75774438196315]
  Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data. Most existing methods suffer from noticeable negative transfer resulting from either the error-prone discriminator network or the unreasonable teacher model. We propose an uncertainty-aware consistency regularization method for cross-domain semantic segmentation.
  arXiv  Detail & Related papers  (2020-04-19T15:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.