Can the Query-based Object Detector Be Designed with Fewer Stages?
This work addresses efficiency issues for users of object detection systems, though it is incremental as it builds on existing query-based methods.
The paper tackles the problem of high computational burden in multi-stage query-based object detectors by proposing GOLO, a two-stage decoding model that achieves competitive performance on the COCO dataset.
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.