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20232026
most citedCan Large Language Models Match the Conclusions of Systematic Reviews?

2 citations · 7 across the 14 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

SalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images

Xiaoxiao Sun, Ruotian Zhang, Junzhe Huang +2

Vision-language models (VLMs) are increasingly used to detect whether AI-generated images contain visible artifacts, yet their ability to analyze such artifacts remains poorly unde…

cs.CV2026

MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models

Ryan D'Cunha, Alejandro Lozano, Xiaoxiao Sun +21

Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling cellular features in microscop…

cs.CV2025

The Impact of Image Resolution on Biomedical Multimodal Large Language Models

Liangyu Chen, James Burgess, Jeffrey J Nirschl +2

Imaging technologies are fundamental to biomedical research and modern medicine, requiring analysis of high-resolution images across various modalities. While multimodal large lang…

cs.CV2025

No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language Models

Min Woo Sun, Alejandro Lozano, Javier Gamazo Tejero +8

Embedding vision-language models (VLMs) are typically pretrained with short text windows (<77 tokens), which forces the truncation of long-format captions. Yet, the distribution of…

cs.CV2025

MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research

James Burgess, Jeffrey J Nirschl, Laura Bravo-Sánchez +20

Scientific research demands sophisticated reasoning over multimodal data, a challenge especially prevalent in biology. Despite recent advances in multimodal large language models (…

cs.CV2025

Video Action Differencing

James Burgess, Xiaohan Wang, Yuhui Zhang +5

How do two individuals differ when performing the same action? In this work, we introduce Video Action Differencing (VidDiff), the novel task of identifying subtle differences betw…

cs.CV2025★ 1 cited

BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature

Alejandro Lozano, Min Woo Sun, James Burgess +13

The development of vision-language models (VLMs) is driven by large-scale and diverse multimodal datasets. However, progress toward generalist biomedical VLMs is limited by the lac…

cs.CV2025

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation

Yuhui Zhang, Yuchang Su, Yiming Liu +9

The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation. However, current visual question answering (VQA) benchmarks often depend on open-en…

cs.CV2024

μ-Bench: A Vision-Language Benchmark for Microscopy Understanding

Alejandro Lozano, Jeffrey Nirschl, James Burgess +4

Recent advances in microscopy have enabled the rapid generation of terabytes of image data in cell biology and biomedical research. Vision-language models (VLMs) offer a promising…

cs.CV2023★ 2 cited

Viewpoint Textual Inversion: Discovering Scene Representations and 3D View Control in 2D Diffusion Models

James Burgess, Kuan-Chieh Wang, Serena Yeung-Levy

Text-to-image diffusion models generate impressive and realistic images, but do they learn to represent the 3D world from only 2D supervision? We demonstrate that yes, certain 3D s…