Object detection is pivotal in computer vision, yet its immense computational demands make deployment slow and power-hungry, motivating quantization. However, task-irrelevant morphologies such as background clutter and sensor noise induce redundant activations (or anomalies). These anomalies expand activation ranges and skew activation distributions toward task-irrelevant responses, complicating bit allocation and weakening the preservation of informative features. Without a clear criterion to distinguish anomalies, suppressing them can inadvertently discard useful information. To address this, we present InlierQ, an inlier-centric post-training quantization approach that separates anomalies from informative inliers. InlierQ computes gradient-aware volume saliency scores, classifies each volume as an inlier or anomaly, and fits a posterior distribution over these scores using the Expectation-Maximization (EM) algorithm. This design suppresses anomalies while preserving informative features. InlierQ is label-free, drop-in, and requires only 64 calibration samples. Experiments on the COCO and nuScenes benchmarks show consistent reductions in quantization error for camera-based (2D and 3D) and LiDAR-based (3D) object detection.

翻译：目标检测是计算机视觉中的关键任务，但其巨大的计算需求导致部署缓慢且功耗高，这推动了量化技术的应用。然而，任务无关的形态（如背景杂波和传感器噪声）会引发冗余激活（或异常值）。这些异常值会扩大激活范围，并使激活分布偏向任务无关的响应，从而复杂化比特分配并削弱信息特征的保留。由于缺乏明确区分异常值的标准，抑制它们可能无意中丢弃有用信息。为解决这一问题，我们提出了InlierQ，一种以内点为中心的训练后量化方法，能够将异常值与信息性内点分离。InlierQ计算梯度感知的体积显著性分数，将每个体积分类为内点或异常值，并使用期望最大化（EM）算法在这些分数上拟合后验分布。该设计在抑制异常值的同时保留了信息特征。InlierQ无需标签、即插即用，且仅需64个校准样本。在COCO和nuScenes基准测试上的实验表明，该方法在基于相机（2D和3D）和基于LiDAR（3D）的目标检测中均能持续降低量化误差。