SegErase: Dual-Output Concept Unlearning for Text-Prompted Segmentation

Text-promptable segmenters learn open-vocabulary grounding from web-scale data and can consequently be prompted to segment sensitive or proprietary concepts. Existing unlearning methods reshape a single global decision, such as a predicted label or a synthesized image. However, unlearning for segmenters is a dense prediction problem governed by two complementary outputs: pixel-wise mask logits determine where a concept is segmented, while an emission score determines whether its mask is returned. We therefore introduce \segerase, a concept-unlearning framework that jointly unlearns both outputs. Concept Attention-Guided Unlearning concentrates mask-side unlearning on pixels most associated with the target. Teacher-Relative Score Unlearning applies a bounded reduction to target scores while anchoring retained and background predictions to a frozen teacher. Extensive experiments on SAM 3 and COSINE demonstrate that \segerase attains a more favorable unlearning-retention balance than existing unlearning methods across standard concepts, unseen prompt reformulations, and multi-target settings.