@inproceedings{
qi2024fetch,
title={Fetch and Forge: Efficient Dataset Condensation for Object Detection},
author={Ding Qi and Jian Li and Jinlong Peng and Bo Zhao and Shuguang Dou and Jialin Li and Jiangning Zhang and Yabiao Wang and Chengjie Wang and Cairong Zhao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m8MElyzuwp}
}
Dataset condensation (DC) is an emerging technique capable of creating compact synthetic datasets from large originals while maintaining considerable performance. It is crucial for accelerating network training and reducing data storage requirements. 
However, current research on DC mainly focuses on image classification, with less exploration of object detection.
This is primarily due to two challenges: (i) the multitasking nature of object detection complicates the condensation process, and (ii) Object detection datasets are characterized by large-scale and high-resolution data, which are difficult for existing DC methods to handle.
As a remedy, we propose DCOD, the first dataset condensation framework for object detection. It operates in two stages: Fetch and Forge, initially storing key localization and classification information into model parameters, and then reconstructing synthetic images via model inversion. 
For the complex of multiple objects in an image, we propose Foreground Background Decoupling to centrally update the foreground of multiple instances and Incremental PatchExpand to further enhance the diversity of foregrounds.
Extensive experiments on various detection datasets demonstrate the superiority of DCOD. Even at an extremely low compression rate of 1\%, we achieve 46.4\% and 24.7\% $\text{AP}_{50}$ on the VOC and COCO, respectively, significantly reducing detector training duration.
Fetch and Forge: Efficient Dataset Condensation for Object Detection
Ding Qi Jian Li Jinlong Peng Bo Zhao Shuguang Dou Jialin Li Jiangning Zhang Yabiao Wang Chengjie Wang Cairong Zhao