Knowledge Distillation for Object Detection: From Generic To Remote Sensing Datasets

Abstract

Knowledge distillation, a well-known model compression technique, is an active research area in both computer vision and remote sensing communities. In this paper, we evaluate in a remote sensing context various off-the-shelf object detection knowledge distillation methods which have been originally developed on generic computer vision datasets such as Pascal VOC. In particular, methods covering both logit mimicking and feature imitation approaches are applied for vehicle detection using the well-known benchmarks such as xView and VEDAI datasets. Extensive experiments are performed to compare the relative performance and interrelationships of the methods. Experimental results show high variations and confirm the importance of result aggregation and cross validation on remote sensing datasets.

Locations

• arXiv (Cornell University) - View - PDF
• IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium - View