{
  "id": 17918,
  "url": "https://arxiv.org/abs/2608.07405v1",
  "title": "GeoDistill-Refine: Silhouette-First Geometry Distillation for Annotation-Free Spacecraft Segmentation",
  "summary": "Foundation segmentation models can provide supervision for spacecraft imagery without manual training masks, but their predictions vary with textual prompts and may contain geometric errors that are amplified during distillation. This paper presents GeoDistill-Refine, a two-stage framework that transfers offline SAM 3 pseudo-masks to a compact segmentation network. Six fixed prompts are fused by an unweighted 50% vote to stabilize the teacher output. The student first learns the foreground silho",
  "authors": "Yonglong Zhang, Zongwu Xie, Yang Liu",
  "category": "research",
  "topics": "children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T16:53:05.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17918",
  "original_url": "https://arxiv.org/abs/2608.07405v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}