  This paper presents an improved DETR detector that maintains a "plain"
nature: using a single-scale feature map and global cross-attention
calculations without specific locality constraints, in contrast to previous
leading DETR-based detectors that reintroduce architectural inductive biases of
multi-scale and locality into the decoder. We show that two simple technologies
are surprisingly effective within a plain design to compensate for the lack of
multi-scale feature maps and locality constraints. The first is a box-to-pixel
relative position bias (BoxRPB) term added to the cross-attention formulation,
which well guides each query to attend to the corresponding object region while
also providing encoding flexibility. The second is masked image modeling
(MIM)-based backbone pre-training which helps learn representation with
fine-grained localization ability and proves crucial for remedying dependencies
on the multi-scale feature maps. By incorporating these technologies and recent
advancements in training and problem formation, the improved "plain" DETR
showed exceptional improvements over the original DETR detector. By leveraging
the Object365 dataset for pre-training, it achieved 63.9 mAP accuracy using a
Swin-L backbone, which is highly competitive with state-of-the-art detectors
which all heavily rely on multi-scale feature maps and region-based feature
extraction. Code is available at https://github.com/impiga/Plain-DETR .
