SGQA: Semantic-Geometric Quality Alignment for Training-Free Few-Shot Instance Segmentation

Compositions of frozen foundation models offer a trainingfree route to instance segmentation, yet a clear gap remains between current systems and their empirical upper bounds. We introduce progressive oracle replacement, a diagnostic procedure that decomposes this gap into stage-level components, and find that the scoring stage accounts for most of it. Across diverse encoders and mask generators, we trace this gap to Quality Misalignment: a mismatch between semantic matching confidence and mask geometric fidelity that yields ranking errors when the two signals are used independently. This pattern persists across all tested configurations, suggesting that it is intrinsic to decoupled scoring. We therefore propose Semantic-Geometric Quality Alignment (SGQA), which replaces decoupled scoring with a single quality score formed by the geometric mean of the two signals and adds no hyperparameters to the scoring formula. Under a fixed backbone of DINOv2 ViT-L paired with SAM2 Hiera-L, SGQA outperforms existing training-free methods and matches or exceeds fine-tuning-based methods with comparable or smaller backbones.