Benchmarking Vision-Language Models on Synapse Detection and Proofreading in Connectomics
We benchmarked vision-language models (VLMs) on the decisions annotators take when inspecting electron microscopy images in connectomics: synapse detection (presence and polarity) and proofreading (split errors and merge errors). For synapse detection, 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 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 he...
| Authors | Yicong Li, Junjie Wang, Leander Lauenburg et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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