A comprehensive hands-on course developed with Arm University covering optimization of generative AI workloads across Arm architectures. Features three practical labs spanning mobile devices, cloud servers, and edge-to-cloud deployment comparisons using Raspberry Pi 5 and AWS Graviton.
Test your own place-recognition skills against state-of-the-art open- and closed-source VLMs in this interactive GeoGuessr-style game.
A compact and efficient approach for Visual Place Recognition using extreme low-bit quantization and progressive distillation.
VSLAMs a streamlined Python implementation of Stereo Visual SLAM. It leverages libraries such as numpy, opencv, and scipy for feature detection, tracking, matching, motion estimation, and optimization—all designed with the KITTI dataset in mind.
An adaptive ternary-quantized Vision Transformer that lets robots dial accuracy versus compute on the fly—shrinking model size 5 × and cutting inference operations by up to 40 % while maintaining state-of-the-art Recall @ 1.
Exploring the growing privacy risks posed by Vision-Language Models that can pinpoint locations from images, and our research into responsible geolocation AI.