Recti-Q: Feature-Space Rectification for Robust Quantized Perception in Edge Robotics
A new preprint demonstrates that post-training quantization (PTQ) of vision models for edge robotics can significantly reduce robustness to real-world distribution shifts, such as sensor noise and severe weather, even when in-distribution accuracy is maintained. The authors introduce Recti-Q, a lightweight feature-space rectification framework that restores much of the lost robustness with minimal parameter and compute overhead, and supports efficient over-the-air patching for deployed robotic fleets.
Why it matters: This work highlights and addresses a critical robustness gap in quantized perception models for edge robotics, improving reliability in unpredictable real-world environments.
Full story at: arXiv Computer Vision ↗