Inlier-Centric Post-Training Quantization for Object Detection Models
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arXiv
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| Format: | Preprint |
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2026
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| author | Kim, Minsu Lee, Dongyeun Yu, Jaemyung Hur, Jiwan Kim, Giseop Kim, Junmo |
| author_facet | Kim, Minsu Lee, Dongyeun Yu, Jaemyung Hur, Jiwan Kim, Giseop Kim, Junmo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03472 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inlier-Centric Post-Training Quantization for Object Detection Models Kim, Minsu Lee, Dongyeun Yu, Jaemyung Hur, Jiwan Kim, Giseop Kim, Junmo Computer Vision and Pattern Recognition 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. |
| title | Inlier-Centric Post-Training Quantization for Object Detection Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.03472 |