Fine-grained vehicle recognition (FGVR) is an essential fundamental
technology for intelligent transportation systems, but very difficult because
of its inherent intra-class variation. Most previous FGVR studies only focus on
the intra-class variation caused by different shooting angles, positions, etc.,
while the intra-class variation caused by image noise has received little
attention. This paper proposes a progressive multi-task anti-noise learning
(PMAL) framework and a progressive multi-task distilling (PMD) framework to
solve the intra-class variation problem in FGVR due to image noise. The PMAL
framework achieves high recognition accuracy by treating image denoising as an
additional task in image recognition and progressively forcing a model to learn
noise invariance. The PMD framework transfers the knowledge of the PMAL-trained
model into the original backbone network, which produces a model with about the
same recognition accuracy as the PMAL-trained model, but without any additional
overheads over the original backbone network. Combining the two frameworks, we
obtain models that significantly exceed previous state-of-the-art methods in
recognition accuracy on two widely-used, standard FGVR datasets, namely
Stanford Cars, and CompCars, as well as three additional surveillance
image-based vehicle-type classification datasets, namely Beijing Institute of
Technology (BIT)-Vehicle, Vehicle Type Image Data 2 (VTID2), and Vehicle Images
Dataset for Make Model Recognition (VIDMMR), without any additional overheads
over the original backbone networks. The source code is available at
this https URL

本文提出了一种渐进式多任务抗噪声学习（PMAL）框架和一种渐进式多任务蒸馏（PMD）框架，以解决细粒度车辆识别中由图像噪声引起的类内差异问题。通过将图像去噪视为图像识别中的一项额外任务，并渐进地使模型学习噪声不变性，PMAL 框架实现了较高的识别准确性。PMD 框架将 PMAL 训练模型的知识转移到原始骨干网络中，产生一个具有与 PMAL 训练模型相同识别准确性的模型，但没有原始骨干网络的额外开销。结合这两个框架，在 Stanford Cars 和 CompCars 等两个广泛使用的标准细粒度车辆识别数据集，以及北京理工大学（BIT）车辆、Vehicle Type Image Data 2（VTID2）和 Vehicle Images Dataset for Make Model Recognition（VIDMMR）等三个监控图像车辆类型分类数据集上，获得了显著超过以往最先进方法的模型，而没有原始骨干网络的额外开销。