提出新量化方法，精准区分有用信号与干扰噪声，提升检测模型精度
Inlier-Centric Post-Training Quantization for Object Detection Models
- 基于梯度感知的显著性评分，识别并分离异常激活
- 仅用64个样本即可完成校准，误差显著降低
- 无需标签、即插即用，适用于2D/3D视觉与激光雷达检测
目标检测在计算机视觉中至关重要，但计算开销大，亟需量化压缩。然而背景杂波和传感器噪声等无关特征会引入冗余激活（异常），扩大激活范围并扭曲分布，导致比特分配困难，削弱关键特征保留。现有方法缺乏有效区分异常与有用信息的标准，抑制异常易误删重要信息。为此，我们提出InlierQ，一种以正常数据为中心的后训练量化方法。该方法计算梯度感知的体积显著性分数，通过期望最大化（EM）算法拟合分数后验分布，分类每个体积为正常或异常，并抑制异常。InlierQ无需标签、可直接部署，仅需64个校准样本。在COCO和nuScenes基准上的实验表明，该方法在基于相机（2D/3D）和激光雷达（3D）的目标检测任务中均实现了量化误差的持续下降。
原文摘要 · Abstract (English)
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.
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