arXiv cs.AI· Yicong Li, Junjie Wang, Leander Lauenburg, Ella Hugie, Alexandra Irger, Wanhua Li, Donglai Wei, Hanspeter Pfister·· 3 小时前AI 评分35
连接组学中视觉语言模型突触与神经元校对基准评测
Benchmarking Vision-Language Models on Synapse and Neuron Proofreading in Connectomics
AI 导读
arXiv 一项研究基准评测了视觉语言模型（VLM）在连接组学电镜图像校对中的表现，涵盖突触校对（存在性与极性）和神经元校对（分裂与合并错误）。
正文 · 原文
Abstract:We benchmarked vision-language models (VLMs) on the decisions annotators take when inspecting electron microscopy images in connectomics: synapse proofreading (presence and polarity) and neuron proofreading (split errors and merge errors). For synapse proofreading, we evaluated 19 open and 2 closed models across various architectures and sizes under zero-shot, four-shot in-context learning and LoRA settings, against specialist models, on datasets constructed by us using public resources. For neuron proofreading, we evaluated 3 open and 2 closed models on the ConnectomeBench2 dataset, with cross-species transfer from fly and mouse to human and zebrafish. Most models were at chance zero-shot; a few examples helped mainly the closed and largest open ones. LoRA on a few thousand labels brought open models level with specialist models. When evaluated on unseen species, the best adapted VLMs outperformed specialist models in identifying merge errors. The project will be publicly available upon acceptance.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.36492 [cs.CV] |
| (or arXiv:2609.36492v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2609.36492 arXiv-issued DOI via DataCite |
Submission history
From: Yicong Li [view email]
[v1]
Tue, 29 Sep 2026 01:48:42 UTC (561 KB)
[v2]
Fri, 23 Oct 2026 05:15:15 UTC (561 KB)
来源：arXiv cs.AI · arxiv.org