---
title: "Single-Annotation, Multi-Hypothesis Segmentation with Semantic-Spatial Residual Banks"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.41069v1"
permalink: "https://feed.astralor.com/items/itm_0fc7a03027ce6e663da031d3"
published: "2026-10-29T23:32:50.000Z"
score: 54
channel: "ai"
tags: ["推理效率","RAG","Single-Annotation","Multi-Hypothesis Segmentation","Semantic-Spatial Residual Banks","Multi-rater","Our","Banks"]
---

# Single-Annotation, Multi-Hypothesis Segmentation with Semantic-Spatial Residual Banks

## 速览

Multi-rater segmentation datasets show that a single image can admit several valid masks, yet most training sets expose only one annotation per image. We study single-annotation multi-hypothesis segmentation: each train…

## 关键点

- Multi-rater segmentation datasets show that a single image can admit several valid masks, yet most training sets expose only one annotation per image.
- We study single-annotation multi-hypothesis segmentation: each training image exposes one fixed mask, while the model must return a finite bank that covers held-out human annotations at test time.
- Our experiments reveal a representation tradeoff.

## 全文

Multi-rater segmentation datasets show that a single image can admit several valid masks, yet most training sets expose only one annotation per image. We study single-annotation multi-hypothesis segmentation: each training image exposes one fixed mask, while the model must return a finite bank that covers held-out human annotations at test time. Our experiments reveal a representation tradeoff. Banks learned directly from images recover variations in local boundaries and extent but lose structural hypotheses, whereas frozen semantic features preserve structural grouping but underrepresent spatial variation. We introduce the Semantic-Spatial Residual Bank (SSRB), which freezes a semantic bank trained with balanced assignments and learns a residual from the image for each hypothesis. Across five medical endpoints, SSRB improves coverage of held-out masks at $K=8$ over a Probabilistic U-Net under the same one-mask constraint. On LIDC and LIDC-hard, it also produces substantially more distinct useful masks. On BSDS, it preserves the semantic bank's performance in grouping regions. Its advantage grows as more masks are returned, showing that residuals learned for corresponding heads improve coverage of local annotation variation while preserving how regions are grouped.

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