Computer Vision

Letting the Neural Code Speak: Automated Characterization of Monkey Visual Neurons Through Human Language

We develop digital twins of V1 and V4 neurons and use generative models to translate neural activity patterns into semantic descriptions, driving 96.1% of V4 neurons to extreme activation levels with synthesized images based on linguistic descriptions.

Estimating label quality and errors in semantic segmentation data via any model

The soft-minimum of the model-estimated likelihoods of each pixel’s annotated class – that is particularly effective to identify images that are mislabeled, across multiple types of annotation error