[Submitted on 17 Oct 2026]
Title:Uncertainty Gates the Gradient: Adaptive Updates for Generalized Few-Shot 3D Segmentation
View a PDF of the paper titled Uncertainty Gates the Gradient: Adaptive Updates for Generalized Few-Shot 3D Segmentation, by Yifei Zhao and 5 other authors
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Abstract:In generalized few-shot 3D point cloud segmentation, support annotations are often compromised by sparsity and occlusion. Blindly adapting to such low quality samples risks learning spurious geometric patterns rather than robust novel concepts. Although 3D Vision-Language Models have been introduced to sup plement supervision, the potential of uncertainty to actively govern adaptation dynamics remains underexplored. Existing signal-level methods like loss reweight ing fail to prevent unreliable episodes from steering the optimization trajectory. To address this, we propose Uncertainty-Gated Adaptive (UGA), to the best of our knowledge the first framework that elevates uncertainty to update-level gating for generalized few-shot 3D point cloud segmentation. The core mod ule, Uncertainty-Gated Update (UG-Update), estimates episodic reliability via intra-class dispersion over merged support and pseudo-labeled novel supervision, and selectively gates residual gradient updates. This encourages the model to learn novel semantics from reliable episodes while attenuating updates during unreliable ones. Complementing this, we introduce an Uncertainty-Based Pseudo Label Filter (UB-Filter) to enforce learning from high-confidence dense signals. On ScanNet200 and ScanNet++ (1-/5-shot), UGA sets new state of the art with an average +3.08 HM IoU over the strongest prior baseline.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.26909 [cs.CV]
(or arXiv:2610.26909v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.26909
arXiv-issued DOI via DataCite
Submission history
From: Yifei Zhao [view email] [v1] Sat, 17 Oct 2026 04:05:32 UTC (8,572 KB)
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