论文标题
局部功能与低端设备的变压器匹配
Local Feature Matching with Transformers for low-end devices
论文作者
论文摘要
LOFTR ARXIV：2104.00680是一种有效的深度学习方法，可在图像对中找到适当的本地特征匹配。本文报告了这种方法的优化，以在计算性能较低和内存有限的设备上工作。原始的LOFTR方法基于Resnet Arxiv：1512.03385头和两个基于线性变压器ARXIV的模块：2006.04768体系结构。在介绍的工作中，仅保留粗匹配块，参数的数量大大减少，并且使用知识蒸馏技术对网络进行了训练。比较表明，尽管模型大小显着降低，但与在粗匹配区块中的教师模型相比，该方法允许学生模型获得适当的特征检测精度。此外，本文显示了使模型与NVIDIA Tensorrt运行时兼容所需的其他步骤，并显示了一种优化低端GPU训练方法的方法。
LoFTR arXiv:2104.00680 is an efficient deep learning method for finding appropriate local feature matches on image pairs. This paper reports on the optimization of this method to work on devices with low computational performance and limited memory. The original LoFTR approach is based on a ResNet arXiv:1512.03385 head and two modules based on Linear Transformer arXiv:2006.04768 architecture. In the presented work, only the coarse-matching block was left, the number of parameters was significantly reduced, and the network was trained using a knowledge distillation technique. The comparison showed that this approach allows to obtain an appropriate feature detection accuracy for the student model compared to the teacher model in the coarse matching block, despite the significant reduction of model size. Also, the paper shows additional steps required to make model compatible with NVIDIA TensorRT runtime, and shows an approach to optimize training method for low-end GPUs.
