  The DETR object detection approach applies the transformer encoder and
decoder architecture to detect objects and achieves promising performance. In
this paper, we present a simple approach to address the main problem of DETR,
the slow convergence, by using representation learning technique. In this
approach, we detect an object bounding box as a pair of keypoints, the top-left
corner and the center, using two decoders. By detecting objects as paired
keypoints, the model builds up a joint classification and pair association on
the output queries from two decoders. For the pair association we propose
utilizing contrastive self-supervised learning algorithm without requiring
specialized architecture. Experimental results on MS COCO dataset show that
Pair DETR can converge at least 10x faster than original DETR and 1.5x faster
than Conditional DETR during training, while having consistently higher Average
Precision scores.
