# AttnSeg

Language-guided segmentation usually compresses the referent's location into a single [SEG] hidden-state vector handed to the decoder, a vector with no spatial index while the decoder never sees the expression. AttnSeg instead passes the [SEG] token's attention over image patches as the decoder's dense prompt and supervises that map directly with an attention-guidance objective in two training stages. On Sa2VA, the 4B model raises ReasonSeg gIoU from 57.70 to 64.11 and GS-Eval from 61.91 to 72.11, with gains concentrated on object parts, multiple objects and single objects, and negligible change on stuff regions.

Language-guided segmentation usually compresses the referent's location into a single [SEG] hidden-state vector handed to the decoder, a vector with no spatial index while the decoder never sees the expression. AttnSeg instead passes the [SEG] token's attention over image patches as the decoder's dense prompt and supervises that map directly with an attention-guidance objective in two training stages. On Sa2VA, the 4B model raises ReasonSeg gIoU from 57.70 to 64.11 and GS-Eval from 61.91 to 72.11, with gains concentrated on object parts, multiple objects and single objects, and negligible change on stuff regions.