论文标题
与混合匹配
DETRs with Hybrid Matching
论文作者
论文摘要
一对一的匹配是DETR建立其端到端功能的关键设计，因此对象检测不需要手工制作的NMS（非最大抑制）即可删除重复检测。这种端到端的签名对于DETR的多功能性很重要，并且已将其推广到更广泛的视觉任务。但是，我们注意到，几乎没有分配为正样本的查询，而一对一的匹配显着降低了阳性样品的训练功效。我们提出了一种基于混合匹配方案的简单而有效的方法，该方法将原始的一对一匹配分支与训练过程中的辅助一对一匹配分支结合在一起。我们的混合策略已被证明可以显着提高准确性。在推断中，仅使用原始的一对一匹配分支，从而维持端到端的优点和相同的DETR推理效率。该方法命名为H-detr，它表明，可以在各种视觉任务中始终如一地改进各种代表性的DITR方法，包括变形词，PETRV2，PETR和TransTrack等。该代码可在以下网址找到：https：//github.com/hdetr
One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including DeformableDETR, PETRv2, PETR, and TransTrack, among others. The code is available at: https://github.com/HDETR
