arXiv cs.CL· Hao Shi, Jiafeng Wang, Yuzhuo Fu, Tao Zhang, Jian Bai, Juelu Zhang, Qing Cui, Chao Huang·· 2 天前AI 评分28
ZoomCap：用证据路由视觉缩放实现可恢复的视觉信息迁移
ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information Transfer
AI 导读
ZoomCap 将细粒度图像描述建模为"可恢复的视觉信息迁移"，通过草稿描述路由条件知识检索、OCR 敏感阅读、覆盖缺口恢复与自适应视觉缩放，仅对最复杂的合格区域分配更深裁剪。
正文 · 原文
Abstract:Detailed captions transfer visual information into vision-language model supervision, but quality is easily conflated with length: long single-pass outputs still miss small objects, text, and spatial relations or promote uncertain observations into confident claims. We formulate fine-grained captioning as recoverable visual information transfer, in which a teacher acquires missing evidence under a finite observation budget and converts it into reusable supervision. ZoomCap uses a draft caption to route conditional knowledge retrieval, OCR-sensitive reading, coverage-gap recovery, and adaptive visual zooming, allocating deeper crops only to the most complex eligible region, deriving spatial relations from grounded geometry, and fusing non-duplicate evidence. We also introduce FactCycle, which scores caption-derived atomic claims on two blind anchors--source image (truthfulness) and reconstruction (realizability)--separating fabrication from generator loss claim by claim; human and multi-judge comparisons complement this fact-level view. Against Qwen3.5-397B-A17B captions, ZoomCap achieves an 81.9% human tie-adjusted preference score with a teacher over 10x smaller, and FactCycle counts 52.4 transferred claims per image vs. 31.5 for ScaleCap. The evidence also transfers: in continual pre-training, ZoomCap-450K improves the 11-benchmark average by 1.31-2.38 points over the strongest competing corpus and Ling-Tiny-VL-v3 gains 1.4 points on a production document/OCR monitor suite (1.5 on perception) at 60B tokens; in medical pre-training, 2.64M ZoomCap-captioned images improve five VQA benchmarks by 3.04 points (vs. 1.76 for same-image Gemini-3.1-Pro captions).
| Comments: | 21 pages, 10 figures, 11 tables; technical report. ZoomCap-450K is publicly available at this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Multimedia (cs.MM) |
| ACM classes: | I.2.10; I.2.7 |
| Cite as: | arXiv:2610.21320 [cs.CV] |
| (or arXiv:2610.21320v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.21320 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hao Shi [view email]
[v1]
Thu, 15 Oct 2026 06:10:24 UTC (14,556 KB)
来源：arXiv cs.CL · arxiv.org