Meta researchers just showed off an AI system that decodes human brain activity into real-time images using non-invasive magnetoencephalography (MEG). By aligning brain signals with its DINOv2 vision model, the setup reconstructs what a person sees on the fly. It accurately captures object types, though object placement and fine details still struggle.
Meta just re-licensed its flagship vision model DINOv2 under Apache 2.0, clearing it for commercial products alongside new dense prediction models for segmentation and depth estimation. The team also released FACET, a 32,000-image benchmark built to measure demographic bias in computer vision. Why it matters: developers can finally use DINOv2's visual features in commercial deployments.
Meta just open-sourced DINOv2, a self-supervised computer vision model that matches or beats standard approaches without relying on text captions or labeled data. Its features can be plugged straight into basic linear classifiers for tasks like depth estimation and segmentation, serving as an off-the-shelf multipurpose visual backbone.
