Mitigating Pose–Scale Discrepancy Bias and Reforming Multi-Support Reasoning for Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) often degrades when support and query images exhibit large pose or scale differences since conventional prototype matching operates in geometry-entangled feature spaces. To mitigate this brittleness, we propose a lightweight encoderdecoder framework that disentangles object representations into a geometryinvariant semantic code and a low-dimensional modulation code capturing instance-specific geometric variation. The reconstruction decoder is trained with equivariance constraints so that geometric information gets encoded by the modulation pathway while preserving semantic consistency in a separate branch. As a result, robustness to pose-scale discrepancies is improved before segmentation without explicit cross-image alignment. To further strengthen supervision from limited supports, we introduce a refinement module that synthesizes lightweight augmented views of each support and performs cyclic ensemble refinement to generate more stable predictions. Predictions from multiple supports are then fused using spatially adaptive reliability weighting, producing cleaner and better-aligned query predictions. Across standard FSS benchmarks, our method consistently improves performance - particularly under large viewpoint changes. Ablations confirm that the disentanglement combined with cyclic ensemble and spatial refinement are critical to the gains. Project Website.