Query-based object detectors have made significant advancements since the publication of DETR. However, most existing methods still rely on multi-stage encoders and decoders, or a combination of both. Despite achieving high accuracy, the multi-stage paradigm (typically consisting of 6 stages) suffers from issues such as heavy computational burden, prompting us to reconsider its necessity. In this paper, we explore multiple techniques to enhance query-based detectors and, based on these findings, propose a novel model called GOLO (Global Once and Local Once), which follows a two-stage decoding paradigm. Compared to other mainstream query-based models with multi-stage decoders, our model employs fewer decoder stages while still achieving considerable performance. Experimental results on the COCO dataset demonstrate the effectiveness of our approach.
@article{arxiv.2309.16306,
title = {Can the Query-based Object Detector Be Designed with Fewer Stages?},
author = {Jialin Li and Weifu Fu and Yuhuan Lin and Qiang Nie and Yong Liu},
journal= {arXiv preprint arXiv:2309.16306},
year = {2023}
}