DeDelayed: Deleting Remote Inference Delay via On‑Device Correction – An Easy‑to‑Understand Summary
Introduction Every day, billions of gigabytes of video are captured by smartphones, dash‑cameras, drones, and wearables. This visual data is the fuel for modern breakthroughs in robotics, autonomous driving, remote sensing, and augmented reality. However, the most accurate video‑understanding models—think of them as the “brains” that can label every pixel in a video frame—are huge, requiring powerful GPUs and lots of memory. For devices that run on a battery or have limited compute (e.g., a car’s dash‑cam, a drone’s onboard computer, or a smartwatch), running these models locally is often impossible. The common workaround is cloud offloading: the device streams video to a server, the server runs the heavy model, and the result is sent back. While this solves the compute problem, it introduces a new one—latency. Even with fast 5G or Wi‑Fi, the round‑trip time (encoding, sending, inference, and returning the result) can be tens or hundreds of milliseconds, which is too slow for many real‑time applications such as lane‑keeping assistance or obstacle avoidance. ...