The paper benchmarks vision‑language models (VLMs) on two key tasks in connectomics: synapse detection (presence and polarity) and proofreading (split and merge errors). It evaluates 21 models (19 open, 2 closed) under zero‑shot, few‑shot, and LoRA fine‑tuning, comparing them to specialist models on datasets built from public resources. While most VLMs perform at chance in zero‑shot settings, LoRA fine‑tuning with a few thousand labels brings open models to specialist performance, and adapted VLMs outperform specialists on unseen species for merge‑error detection.
By Yicong Li, Junjie Wang, Leander Lauenburg, Ella Hugie, Alexandra Irger, Wanhua Li, Donglai Wei, Hanspeter Pfister
arXiv:2602. 18094v2 Announce Type: replace-cross Abstract: Existing Visual-Language Models (VLMs) have achieved significant progress by being trained on massive-scale datasets, typically under the assumption that data are independent and identically distributed (IID).
By Ling Lin, Yang Bai, Heng Su, Congcong Zhu, Yaoxing Wang, Yang Zhou, Huazhu Fu, Jingrun Chen
arXiv:2610.09550v1 Announce Type: new
Abstract: Vision-language models increasingly reason through crops, regions, and tool-produced observations. Yet an observation can influence the answer without...
By Huiyao Zhang, Jin Bai, Zilong Su, Rui Guo, Chaofan Qin, Jinze Lv, Wenhui Yu, Hongfei Wang, Ye Li
arXiv:2610.33564v1 Announce Type: new
Abstract: In the era of large-scale pretrained models, zero-shot generalization has increasingly blurred the boundary between known and unknown concepts. However...
By Zhiran Luo, Zhiyong Yang, Zitai Wang, Sicong Li, Qianqian Xu, Qingming Huang
The paper introduces FOCUS, a cross‑dataset benchmark for retinal fundus models that evaluates vision‑only encoders, vision‑language dual encoders, and multimodal large language models across ten public datasets. It measures ranking performance, calibration, subgroup disparities, and image‑quality robustness for tasks such as diabetic retinopathy and glaucomatous optic neuropathy. Results show no single model family dominates, with vision‑only encoders ranking highest on average, while fine‑tuning improves in‑domain performance but varies in external transfer.
By David Restrepo, Chenwei Wu, Luis Filipe Nakayama, Miguel L. Martins, Stergios Christodoulidis, Maria Vakalopoulou, Enzo Ferrante
arXiv:2606. 28551v1 Announce Type: cross Abstract: Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curation strategies.
By Matteo Farina, Vishaal Udandarao, Thao Nguyen, Selim Kuzucu, Maximilian B\"other, Andreas Hochlehnert, Adhiraj Ghosh, Marianna Nezhurina, Karsten Roth, Joschka Struber, Yuhui Zhang, Sebastian Dziadzio, Elaine Sui, Soumya Jahagirdar, Dhruba Ghosh, Hasan Hammoud, Thomas De Min, Simone Caldarella, Jehanzeb Mirza, Sedrick Keh, Mehdi Cherti, Hilde Kuehne, Bernt Schiele, Serena Yeung-Levy, Muhammad Ferjad Naeem, Federico Tombari, Ana Klimovic, Elisa Ricci, Matthias Bethge, Sewoong Oh, Ameya Prabhu, Alessio Tonioni, Jenia Jitsev, Massimiliano Mancini, Ludwig Schmidt, Nikhil Parthasarathy
CellTrack-Bench is a new benchmark designed to evaluate Vision‑Language Models (VLMs) on microscopic cell tracking tasks. It compiles 1,537 problems from 15 standard cell‑tracking datasets across four imaging modalities, totaling 9,041 cell‑linking decisions, and includes a hard subset that highlights shared failures among specialized trackers. The benchmark was used to assess 9 closed‑source and 13 open‑weight VLMs, compare them to specialized trackers, and analyze their failure modes, with all code and data available on GitHub.
By Ilan Theodoro, Jord\~ao Bragantini, Lo\"ic A. Royer
The paper investigates how evaluation protocols and ground‑truth quality affect the classification performance of Multimodal Large Language Models (MLLMs). It identifies and corrects key issues such as discarded out‑of‑list outputs, inflated distractor choices, and poor open‑world mapping, and shows that design choices like batch size, image ordering, and text encoder selection significantly influence accuracy. Using a multilabel reannotation of 625 ImageNet‑1k classes (ReGT), the study finds that corrected labels can boost MLLM performance by up to 10.8%, narrowing the gap with supervised models, and demonstrates that MLLMs can assist human annotators in about half of difficult cases.
By Nikita Kisel, Illia Volkov, Klara Janouskova, Jiri Matas
arXiv:2505.07675v3 Announce Type: replace-cross
Abstract: Semi-supervised learning (SSL) has emerged as a practical solution for addressing data scarcity challenges by leveraging unlabeled data. Rece...
By Seongjae Kang, Dong Bok Lee, Hyungjoon Jang, Sung Ju Hwang
arXiv:2608.23253v1 Announce Type: cross
Abstract: Vision-language models typically encode an image into hundreds of visual tokens, incurring substantial inference latency and GPU memory overhead. Exi...
By Taoyu Qian, Qi Wang, Daqian Shi, Yuanhao Jiang, Shang Gao, Hualong Yu
arXiv:2608.29395v1 Announce Type: new
Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...
By Pedram MohajerAnsari, Amir Salarpour, Run Wang, Mert D. Pes\'e
arXiv:2608. 04496v1 Announce Type: cross Abstract: Visual inputs in vision-language models (VLMs) are often encoded into substantially longer token sequences than text, making visual tokens a major bottleneck for efficient inference.
By Chen Zhong, Xiao An, Zijie Wang, Jiepan Li, Guangyi Yang, Wei He