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。

细粒度车辆识别的渐进式多任务抗噪学习和蒸馏框架

Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for  Fine-grained Vehicle Recognition

Large intra-class variation is the result of changes in multiple object
characteristics. Images, however, only show the superposition of different
variable factors such as appearance or shape. Therefore, learning to
disentangle and represent these different characteristics poses a great
challenge, especially in the unsupervised case. Moreover, large object
articulation calls for a flexible part-based model. We present an unsupervised
approach for disentangling appearance and shape by learning parts consistently
over all instances of a category. Our model for learning an object
representation is trained by simultaneously exploiting invariance and
equivariance constraints between synthetically transformed images. Since no
part annotation or prior information on an object class is required, the
approach is applicable to arbitrary classes. We evaluate our approach on a wide
range of object categories and diverse tasks including pose prediction,
disentangled image synthesis, and video-to-video translation. The approach
outperforms the state-of-the-art on unsupervised keypoint prediction and
compares favorably even against supervised approaches on the task of shape and
appearance transfer.

提出了一种基于无监督学习的方法，通过学习物体的不同特征，如表现和形状，来实现特征的分离和表示，并使用等变约束进行训练，适用于任意类别，并在各种任务中表现优异。

无监督基于部件的物体形状和外观解耦

Unsupervised Part-Based Disentangling of Object Shape and Appearance

Inner product-based convolution has been a central component of convolutional
neural networks (CNNs) and the key to learning visual representations. Inspired
by the observation that CNN-learned features are naturally decoupled with the
norm of features corresponding to the intra-class variation and the angle
corresponding to the semantic difference, we propose a generic decoupled
learning framework which models the intra-class variation and semantic
difference independently. Specifically, we first reparametrize the inner
product to a decoupled form and then generalize it to the decoupled convolution
operator which serves as the building block of our decoupled networks. We
present several effective instances of the decoupled convolution operator. Each
decoupled operator is well motivated and has an intuitive geometric
interpretation. Based on these decoupled operators, we further propose to
directly learn the operator from data. Extensive experiments show that such
decoupled reparameterization renders significant performance gain with easier
convergence and stronger robustness.

提出了一种基于内积的离线卷积学习框架，它将内部特征限制为对应于类内变化和语义差异的特征规范和角度，通过将内积重新参数化为解耦形式并将其推广到解耦卷积算子，该算子是解耦网络的构建块。

解耦网络

Decoupled Networks

Fine-Grained Visual Classification (FGVC) datasets contain small sample
sizes, along with significant intra-class variation and inter-class similarity.
While prior work has addressed intra-class variation using localization and
segmentation techniques, inter-class similarity may also affect feature
learning and reduce classification performance. In this work, we address this
problem using a novel optimization procedure for the end-to-end neural network
training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces
overfitting by intentionally {introducing confusion} in the activations. With
PC regularization, we obtain state-of-the-art performance on six of the most
widely-used FGVC datasets and demonstrate improved localization ability. {PC}
is easy to implement, does not need excessive hyperparameter tuning during
training, and does not add significant overhead during test time.

通过引入 Pairwise Confusion 来优化 Fine-Grained Visual Classification 任务的模型训练，减少过拟合和提高分类和定位效果。