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Inferior and Coordinate Distillation for Object Detectors.

Yao Zhang1, Yang Li1, Zhisong Pan1

  • 1School of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China.

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|August 12, 2022
PubMed
Summary

This study introduces a new knowledge distillation method for object detectors, leveraging information from all teacher model layers. This approach enhances student model performance by incorporating prior knowledge and improving feature representation.

Keywords:
inferior distillationknowledge distillationobject detectionrefine module

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Current knowledge distillation methods for object detection are limited, focusing only on corresponding layers.
  • This overlooks valuable information present in preceding layers of the teacher model.

Purpose of the Study:

  • To develop an improved knowledge distillation technique for object detectors.
  • To enhance student model learning by utilizing richer information from the teacher model's feature hierarchy.

Main Methods:

  • Proposed "inferior and coordinate distillation" method, analyzing the guiding effect of teacher model's inferior features on student model's coordinate features.
  • Utilized a "refine module" to align features across layers, extract channel and spatial attention, and strengthen inter-stage feature correlations.

Main Results:

  • Achieved improved mean average precision (mAP) on the COCO dataset for Faster R-CNN, RetinaNet, and FCOS object detectors.
  • Results showed performance gains of 2.1%, 2.4%, and 4.3% respectively, over baseline models using ResNet-50 backbone.

Conclusions:

  • The proposed inferior and coordinate distillation method effectively transfers knowledge from diverse layers of a teacher model.
  • This technique enhances object detection performance by enabling students to learn from both past and new information, alongside "dark knowledge".