论文标题
特征查询R-CNN
Featurized Query R-CNN
论文作者
论文摘要
DETR方法中引入的查询机制正在改变对象检测的范式，最近有许多基于查询的方法获得了强对象检测性能。但是，当前基于查询的检测管道遇到了以下两个问题。首先，需要多阶段解码器来优化随机初始化的对象查询，从而产生较大的计算负担。其次，查询在训练后进行固定，从而导致不满意的概括能力。为了纠正上述问题，我们在更快的R-CNN框架中提出了通过查询生成网络预测的特征对象查询，并开发了一个功能符号的查询R-CNN。可可数据集的广泛实验表明，我们的特征查询R-CNN获得了所有R-CNN探测器的最佳速度准确性权衡，包括最近最新的稀疏R-CNN检测器。该代码可在{https://github.com/hustvl/featurized-queryrcnn上找到。
The query mechanism introduced in the DETR method is changing the paradigm of object detection and recently there are many query-based methods have obtained strong object detection performance. However, the current query-based detection pipelines suffer from the following two issues. Firstly, multi-stage decoders are required to optimize the randomly initialized object queries, incurring a large computation burden. Secondly, the queries are fixed after training, leading to unsatisfying generalization capability. To remedy the above issues, we present featurized object queries predicted by a query generation network in the well-established Faster R-CNN framework and develop a Featurized Query R-CNN. Extensive experiments on the COCO dataset show that our Featurized Query R-CNN obtains the best speed-accuracy trade-off among all R-CNN detectors, including the recent state-of-the-art Sparse R-CNN detector. The code is available at {https://github.com/hustvl/Featurized-QueryRCNN.
