Title: Inlier-Centric Post-Training Quantization for Object Detection Models
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Quantization, Object Detection, Efficiency

Abstract: Object detection is pivotal in high-level robotics, but its immense computational demands make the models slow and power-hungry, underscoring the need for quantization. However, when the quantization is applied in practice, cluttered backgrounds and irregular object morphologies cause redundant activations (or \textit{anomalies}) that inflate precision requirements and waste bit capacity, hindering the preservation of informative features. Moreover, without a clear criterion for defining such anomalies, attempts to exclude or mitigate them often distort useful features. To address this problem, we present InlierQ, an inlier-centric post-training quantization approach that establishes a general criterion to differentiate anomalies from informative \textit{inliers}. Specifically, InlierQ computes gradient-aware volume saliency scores, classifies each volume as an inlier or outlier, and fits a posterior distribution over these scores using the Expectation–Maximization (EM) algorithm. This design effectively suppresses the influence of outliers while preserving informative inlier features. InlierQ is a label-free, drop-in method and uses only 64 samples for calibration. Experiments on the COCO and nuScenes benchmarks demonstrate consistent reductions in quantization errors across camera-based (2D and 3D) and LiDAR-based (3D) object detection.

TL;DR: Estimation of Inlier set for Post-Training Quantization of Object Detection Models