Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach
This work investigates how to leverage fine-grained privileged information—such as masks, saliency maps, and depth cues—available only during training to enhance object detection performance without increasing inference complexity. To this end, it systematically introduces the Learning Using Privileged Information (LUPI) paradigm into object detection for the first time, proposing a model-agnostic teacher–student distillation framework in which a teacher network fuses multimodal privileged signals to guide the training of a student detector. The approach requires no architectural modifications at inference time and integrates seamlessly with mainstream detectors. Experiments on benchmarks including Pascal VOC 2012 and UAV-based litter detection demonstrate consistent and significant improvements in detection accuracy—particularly for medium and large objects—without any increase in model size or inference overhead, thereby validating its generality and effectiveness.