Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach
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.
6960a4995b7998385e639609
Matthias Bartolo
claimed_verified
2026-01-09T08:24:06.946Z
664ae70790135abe9ba76c28
https://cdn-avatars.huggingface.co/v1/production/uploads/664ae70790135abe9ba76c28/S9j90H0hjdJwf4x1kXIV0.png
Matthias Bartolo
mbar0075
user
mbar0075
6960a4995b7998385e63960a
Dylan Seychell
6960a4995b7998385e63960b
Gabriel Hili
6960a4995b7998385e63960c
Matthew Montebello
6960a4995b7998385e63960d
Carl James Debono
6960a4995b7998385e63960e
Saviour Formosa
6960a4995b7998385e63960f
Konstantinos Makantasis
Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach
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.
Learning Using Privileged Information paradigm enhances object detection accuracy by integrating additional training-time information through teacher-student architectures without increasing inference complexity.
Learning Using Privileged Information object detection teacher-student architecture bounding box masks saliency maps depth cues model-agnostic methodology UAV-based litter detection Pascal VOC 2012 ablation studies intermediate weighting