James Burgess

MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research
James Burgess*, Jeffrey J Nirschl*, Laura Bravo-Sánchez*, Alejandro Lozano, Sanket Rajan Gupte, Jesus G. Galaz-Montoya, Yuhui Zhang, Yuchang Su, Disha Bhowmik, Zachary Coman, Sarina M. Hasan, Alexandra Johannesson, William D. Leineweber, Malvika G Nair, Ridhi Yarlagadda, Connor Zuraski, Wah Chiu, Sarah Cohen, Jan N. Hansen, Manuel D Leonetti, Chad Liu, Emma Lundberg, Serena Yeung-Levy
CVPR 2025
project page & blog /  arxiv /  benchmark /  code
*co-first authorship

MicroVQA is an expert-curated benchmark for research-level reasoning in biological microscopy. We also propose a method called RefineBot for removing language shortcuts from multiple-choice VQA.

BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature
Alejandro Lozano*, Min Woo Sun*, James Burgess*, Liangyu Chen, Jeffrey J. Nirschl, Jeffrey Gu, Ivan Lopez, Josiah Aklilu, Anita Rau, Austin Wolfgana Katzer, Collin Chiu, Xiaohan Wang, Alfred Seunghoon Song, Robert Tibshirani, Serena Yeung-Levy
CVPR 2025
project page /  arxiv /  code /  data
*co-first authorship

The BIOMEDICA dataset has 6 million scientific articles and 24 million image-text pairs for training vision-language models in biomedicine. We use it to train state-of-the-art embedding models for biomedical images.

Video Action Differencing
James Burgess, Xiaohan Wang, Yuhui Zhang, Anita Rau, Alejandro Lozano, Lisa Dunlap, Trevor Darrell, Serena Yeung-Levy
ICLR 2025
project page & blog /  paper /  benchmark /  code

We propose Video Action Differencing (VidDiff), a new task for detecting subtle variations in how actions are performed between two videos. We release a benchmark spaning diverse skilled actions, and a baseline method that is a simple agentic workflow.

Viewpoint Textual Inversion: Discovering Scene Representations and 3D View Control in 2D Diffusion Models
James Burgess, Kuan-Chieh Wang, Serena Yeung-Levy
ECCV 2024
project page /  arXiv /  code
Outstanding Paper Award at the ECCV Workshop "Emergent Visual Abilities and Limits of Foundation Models"

We show that 2D diffusion models like StableDiffusion have 3D control in their text input space which we call '3D view tokens'.

Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles
James Burgess, Jeffrey J. Nirschl, Maria-Clara Zanellati, Alejandro Lozano, Sarah Cohen, Serena Yeung-Levy
Nature Communications 2024
paper /  code

Unsupervised shape representations of cells and organelles are erroneously sensitive to image orientation, which we mitigate with equivariant convolutional network encoders in our method, O2VAE.

Global organelle profiling reveals subcellular localization and remodeling at proteome scale
Hein et. al. (including James Burgess)
Cell 2024
bioRxiv /  code

A proteomics map of human subcellular architecture, led by the Chan-Zuckerberg Biohub.