用训练时的额外信息提升目标检测精度，推理无负担。
Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach
- 通过师生架构注入边界框掩码等特权信息
- 在多个数据集上精度显著提升，大中物体效果更明显
- 无需增加推理开销，适合实际部署场景
本文研究在目标检测中引入学习使用特权信息（LUPI）范式，利用训练阶段可用但推理阶段不可见的细粒度描述信息。提出一种通用、模型无关的方法，通过师生架构将边界框掩码、显著性图和深度线索等特权信息注入深度学习目标检测器。在五种主流检测模型及多个公开基准（包括基于无人机的垃圾检测数据集和Pascal VOC 2012）上进行实验，评估对准确率、泛化能力和计算效率的影响。结果表明，经LUPI训练的学生模型始终优于基线，检测精度显著提升，且推理复杂度与模型规模均未增加。对中大型目标的性能提升尤为显著；消融实验显示，教师指导的中间加权可最优平衡特权信息与标准输入的学习。研究证实，该框架是提升资源受限与真实场景下检测系统性能的有效实用策略。
原文摘要 · Abstract (English)
This paper investigates the integration of the Learning Using Privileged Information (LUPI) paradigm in object detection to exploit fine-grained, descriptive information available during training but not at inference. We introduce a general, model-agnostic methodology for injecting privileged information-such as bounding box masks, saliency maps, and depth cues-into deep learning-based object detectors through a teacher-student architecture. Experiments are conducted across five state-of-the-art object detection models and multiple public benchmarks, including UAV-based litter detection datasets and Pascal VOC 2012, to assess the impact on accuracy, generalization, and computational efficiency. Our results demonstrate that LUPI-trained students consistently outperform their baseline counterparts, achieving significant boosts in detection accuracy with no increase in inference complexity or model size. Performance improvements are especially marked for medium and large objects, while ablation studies reveal that intermediate weighting of teacher guidance optimally balances learning from privileged and standard inputs. The findings affirm that the LUPI framework provides an effective and practical strategy for advancing object detection systems in both resource-constrained and real-world settings.
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