Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. View more CV events here.

Transformer-based models have advanced 3D scene reconstruction, but their quadratic attention limits scalability to large scenes. We introduce the Local View Transformer (LVT), which replaces global attention with locality-aware attention over neighboring views, conditioned on relative camera geometry. LVT decodes directly into 3D Gaussian splats with view-dependent color and opacity for high-fidelity rendering. Our approach enables scalable, single-pass reconstruction of large, high-resolution scenes.

He’ll share his “tales from the engine room” - practical insights from operating AI systems at scale, including the challenges of abstraction layers, the realities of data movement and hardware constraints, and how systems thinking is essential for building high-performance, secure, and responsible AI infrastructure.

Often the best way to understand what a tool is truly capable of, is to use in ways it was never intended to be used. This session pushes FiftyOne past its computer vision roots through a series of demos showing how to push the boundaries with FiftyOne. A few practical, some playful, all built with open source code. You'll see how FiftyOne's core building blocks generalize far beyond labeled datasets, and leave with patterns and ideas you can take in your own direction.

Advances in deep learning make very-low-field (VLF) MRI systems a viable alternative for in vivo neuroimaging. Zero-shot super-resolution, self-supervised learning, and generative AI were explored to improve the quality of low-field MRI images. We present a framework for the first deployment of a VLF scanner for imaging Nipah virus-inoculated nonhuman primates (NHPs) using a 0.05 T MRI system. First, a retrospective simulation study assessed the feasibility of imaging NiV infection at low field, followed by a prospective deployment (0.05 T) that enabled longitudinal imaging. The VLF-NiV imaging was characterized by low image quality and included multiple contrasts. A deep learning-based unpaired domain adaptation (CycleGAN) conditioned on acquisition parameters was used to harmonize contrast, and a simulation-based ResUNet model was used to reduce unwanted noise and preserve T2-weighted structural fidelity. We also highlight studies involving zero-shot super-resolution and denoising experiments that are advantageous for accessible neuroimaging.