FeatAug-DETR：通过特征增强丰富DETR的一对多匹配
FeatAug-DETR: Enriching One-to-Many Matching for DETRs with Feature Augmentation
摘要：
一对一匹配是类DETR目标检测框架中的一个关键设计。它使DETR能够执行端到端检测。但也面临着缺乏正样本监督、收敛速度慢等挑战。最近的一些工作提出了一对多匹配机制，以加速训练和提高检测性能。我们重新审视这些方法，并在一个统一的格式，增加对象查询模型。在本文中，我们提出了两种方法，实现一对多的匹配，从不同的角度增强图像或图像特征。第一种方法是通过数据增强的一对多匹配（表示为DataAug-DETR）。它对图像进行空间变换，并在同一批训练中包含每个图像的多个增强版本。这样一个简单的增强策略已经实现了一对多的匹配，并令人惊讶地提高了DETR的性能。第二种方法是通过特征增强的一对多匹配（表示为特征增强-DETR）。与DataAug-DETR不同，它增加了图像特征而不是原始图像，并在同一批中包含多个增强特征，以实现一对多匹配。DETR显著加速了DETR训练，提高了检测性能，同时保持推理速度不变。我们进行了广泛的实验，以评估所提出的方法的有效性DETR的变种，包括DAB-DETR，变形DETR，和H-变形DETR。在没有额外的训练数据的情况下，XNUMX Aug-DETR将Deformable-DETR的训练收敛周期缩短到24个epoch，并在以Swin-L为骨干的COCO val 2017集上实现了58.3 AP。
英文摘要：
One-to-one matching is a crucial design in DETR-like object detection frameworks. It enables the DETR to perform end-to-end detection. However, it also faces challenges of lacking positive sample supervision and slow convergence speed. Several recent works proposed the one-to-many matching mechanism to accelerate training and boost detection performance. We revisit these methods and model them in a unified format of augmenting the object queries. In this paper, we propose two methods that realize one-to-many matching from a different perspective of augmenting images or image features. The first method is One-to-many Matching via Data Augmentation (denoted as DataAug-DETR). It spatially transforms the images and includes multiple augmented versions of each image in the same training batch. Such a simple augmentation strategy already achieves one-to-many matching and surprisingly improves DETR's performance. The second method is One-to-many matching via Feature Augmentation (denoted as FeatAug-DETR). Unlike DataAug-DETR, it augments the image features instead of the original images and includes multiple augmented features in the same batch to realize one-to-many matching. FeatAug-DETR significantly accelerates DETR training and boosts detection performance while keeping the inference speed unchanged. We conduct extensive experiments to evaluate the effectiveness of the proposed approach on DETR variants, including DAB-DETR, Deformable-DETR, and H-Deformable-DETR. Without extra training data, FeatAug-DETR shortens the training convergence periods of Deformable-DETR to 24 epochs and achieves 58.3 AP on COCO val2017 set with Swin-L as the backbone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
