@inproceedings{
shengzhang2024information,
title={Information Fusion with Knowledge Distillation for Fine-grained Remote Sensing Object Detection},
author={Shengzhang and Xi Yang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=IkELeZGD2U}
}
Fine-grained remote sensing object detection aims to locate and identify specific targets with variable scale and orientation from complex background in the high-resolution and wide-swath images, which needs requirement of high precision and real-time processing simultaneously. Although traditional knowledge distillation technology show its effectiveness in model compression and accuracy preservation for natural images, the challenges of heavy background noise and intra-class similarity faced by remote sensing images limits the knowledge quality of teacher model and the learning ability of student model. To address these issues, we propose an Information Fusion with Knowledge Distillation (IFKD) method that enhances the student model's performance by integrating information from external images, frequency domain, and hyperbolic space. Firstly, we propose an external interference enhancement (EDE) module, which utilizes MobileSAM introducing information from external to enrich teachers' knowledge set, compete with teachers for the right to cultivate students, and weaken students' dependence on teachers. Secondly, to strengthen the representation of key features and improve the quality of knowledge, a frequency domain reconstruction (FDR) module is proposed, which is mainly performed by resampling the low-frequency background frequency to suppress the interference of background noise. Finally, aiming at the problem of intra-class similarity, hyperbolic similarity mask (HSM) module is designed to magnify intra-class differences and guide students to analyze teachers' knowledge based on the exponential growth of hyperbolic spatial ability. Experiments on the optical ShipRSImageNet and SAR Aircraft-1.0 datasets verify that the IFKD method significantly enhances performance in fine-grained recognition tasks compared to existing distillation techniques. Among them, 65.8% $AP_{50}$ can be improved by 2.6% on ShipRSImageNet dataset, and 81.4% $AP_{50}$ can be improved by 1.4% on SAR Aircraft-1.0.
Information Fusion with Knowledge Distillation for Fine-grained Remote Sensing Object Detection
Shengzhang Xi Yang