论文标题
配对detr：对比度学习加快了detr训练
Pair DETR: Contrastive Learning Speeds Up DETR Training
论文作者
论文摘要
DETR对象检测方法应用了变压器编码器和解码器体系结构来检测对象并实现有希望的性能。在本文中，我们提出了一种简单的方法来通过使用表示技术来解决DETR的主要问题，即慢速收敛。在这种方法中，我们使用两个解码器将对象边界框视为一对关键点，左上角和中心。通过将对象视为配对的关键点，该模型在两个解码器的输出查询上建立了联合分类和配对关联。对于一对关联，我们建议利用对比度的自我监督学习算法，而无需专门的体系结构。 MS可可数据集的实验结果表明，对在训练过程中，对比原始DETR的收敛速度比原始DEDR的速度至少比原始DEDR快10倍，而有条件的DETR比训练过程快1.5倍，同时始终更高的平均精度得分。
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.
