✂️ Segmentation¶

🧠 NeurIPS2026 · 3 paper notes

🔥 Top topics: Segmentation ×2 · Adversarial Robustness ×2

Preference-Guided Adaptation for Open-Vocabulary Semantic Segmentation via Prompt Disagreement: The paper converts localized segmentation disagreement between prompt templates into binary supervision and adapts open-vocabulary segmentation with region-localized preference optimization and outside-region consistency; with a ground-truth-based preference oracle, CAT-Seg-L improves its mean MESS mIoU from 35.26 to 45.88.
Universal Cross-Prompt Adversarial Attacks on Promptable Concept Segmentation: AdvPCS examines vulnerabilities in promptable concept segmentation through prompt variation, concept perception, and temporal memory, reducing three-model average video mIoU on SA-CO with text prompts from 70.24% to 4.61% in controlled digital-input experiments, without establishing failure guarantees for arbitrary prompts, models, or real-world inputs.
When Noise Meets Long-Tail: Feature-Threshold Dual Calibration for Robust Pseudo-Labeling: FTC-Seg combines prototype-based residual feature reconstruction with pseudo-label thresholds driven by labeled accuracy and class-distribution bias in teacher–student segmentation, improving DINOv2-S over UniMatch V2 by 2.09–8.54 mIoU percentage points across four benchmarks with 2% labels, without winning every configuration.