SAME OBJECT, DIFFERENT VERDICT: CLASS-SPECIFIC LOCATION PRIORS IN SEGMENTATION

Moving an object can change a segmentation model’s verdict even when its pixels are unchanged. We study these class-specific location preferences through object placements, controlled training, and label-free prediction maps. On 400 reserved ADE20K sources, 65–67% of objects that reach IoU ≥ 0.5 somewhere fall below it at another placement in the same background. The effect’s direction agrees with the class’s training layout for 69–72% of objects with nonzero vertical contrast. Correlated training induces comparable placement penalties in zero-padded and exactly circularly equivariant CNNs: joint translation equivariance does not prevent learning object–background relationships. Architecture changes the preference’s persistence under corruption and its visibility in average prediction maps. Finally, translation augmentation reduces whole-scene shift penalties by 55% and 38% in DeepLabV3+ and SegFormer, while largely preserving their class-specific placement profiles. A better scene-shift score therefore does not certify object-placement robustness. Evaluating spatial reliability requires both, alongside the class-directed effects that pooled averages conceal.