2 citations · 2 across the 5 of their papers we have counts for
5 papers
Robust Steganography from Large Language Models
Neil Perry, Sanket Gupte, Nishant Pitta +1
Recent steganographic schemes, starting with Meteor (CCS'21), rely on leveraging large language models (LLMs) to resolve a historically-challenging task of disguising covert commun…
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 (…
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…
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…
On Optimizing Human-Machine Task Assignments
Andreas Veit, Michael Wilber, Rajan Vaish +48
When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the cro…