1 citations · 2 across the 8 of their papers we have counts for
8 papers
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…
Systematic Evaluation of Large Vision-Language Models for Surgical Artificial Intelligence
Anita Rau, Mark Endo, Josiah Aklilu +6
Large Vision-Language Models offer a new paradigm for AI-driven image understanding, enabling models to perform tasks without task-specific training. This flexibility holds particu…
A Large-Scale Vision-Language Dataset Derived from Open Scientific Literature to Advance Biomedical Generalist AI
Alejandro Lozano, Min Woo Sun, James Burgess +16
Despite the excitement behind biomedical artificial intelligence (AI), access to high-quality, diverse, and large-scale data - the foundation for modern AI systems - is still a bot…
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…
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…
Zero-shot Action Localization via the Confidence of Large Vision-Language Models
Josiah Aklilu, Xiaohan Wang, Serena Yeung-Levy
Precise action localization in untrimmed video is vital for fields such as professional sports and minimally invasive surgery, where the delineation of particular motions in record…
Depth-guided NeRF Training via Earth Mover's Distance
Anita Rau, Josiah Aklilu, F. Christopher Holsinger +1
Neural Radiance Fields (NeRFs) are trained to minimize the rendering loss of predicted viewpoints. However, the photometric loss often does not provide enough information to disamb…
Revisiting Active Learning in the Era of Vision Foundation Models
Sanket Rajan Gupte, Josiah Aklilu, Jeffrey J. Nirschl +1
Foundation vision or vision-language models are trained on large unlabeled or noisy data and learn robust representations that can achieve impressive zero- or few-shot performance…