Lane detection is crucial for vehicle localization which makes it the
foundation for automated driving and many intelligent and advanced driving
assistant systems. Available vision-based lane detection methods do not make
full use of the valuable features and aggregate contextual information,
especially the interrelationships between lane lines and other regions of the
images in continuous frames. To fill this research gap and upgrade lane
detection performance, this paper proposes a pipeline consisting of self
pre-training with masked sequential autoencoders and fine-tuning with
customized PolyLoss for the end-to-end neural network models using
multi-continuous image frames. The masked sequential autoencoders are adopted
to pre-train the neural network models with reconstructing the missing pixels
from a random masked image as the objective. Then, in the fine-tuning
segmentation phase where lane detection segmentation is performed, the
continuous image frames are served as the inputs, and the pre-trained model
weights are transferred and further updated using the backpropagation mechanism
with customized PolyLoss calculating the weighted errors between the output
lane detection results and the labeled ground truth. Extensive experiment
results demonstrate that, with the proposed pipeline, the lane detection model
performance on both normal and challenging scenes can be advanced beyond the
state-of-the-art, delivering the best testing accuracy (98.38%), precision
(0.937), and F1-measure (0.924) on the normal scene testing set, together with
the best overall accuracy (98.36%) and precision (0.844) in the challenging
scene test set, while the training time can be substantially shortened.

本研究提出利用自我预训练和自定义多连续帧图像的 PolyLoss 微调深度网络模型的方法，使车道检测性能得到进一步提高。得到了 98.38% 的最佳测试精度和 0.937 的最佳精确度，训练时间大大缩短。

自编码器掩码序列自预训练与自定义 PolyLoss 微调实现鲁棒车道检测

Robust Lane Detection through Self Pre-training with Masked Sequential  Autoencoders and Fine-tuning with Customized PolyLoss

Test-time adaptation (TTA) refers to adapting neural networks to distribution
shifts, with access to only the unlabeled test samples from the new domain at
test-time. Prior TTA methods optimize over unsupervised objectives such as the
entropy of model predictions in TENT [Wang et al., 2021], but it is unclear
what exactly makes a good TTA loss. In this paper, we start by presenting a
surprising phenomenon: if we attempt to meta-learn the best possible TTA loss
over a wide class of functions, then we recover a function that is remarkably
similar to (a temperature-scaled version of) the softmax-entropy employed by
TENT. This only holds, however, if the classifier we are adapting is trained
via cross-entropy; if trained via squared loss, a different best TTA loss
emerges. To explain this phenomenon, we analyze TTA through the lens of the
training losses's convex conjugate. We show that under natural conditions, this
(unsupervised) conjugate function can be viewed as a good local approximation
to the original supervised loss and indeed, it recovers the best losses found
by meta-learning. This leads to a generic recipe that can be used to find a
good TTA loss for any given supervised training loss function of a general
class. Empirically, our approach consistently dominates other baselines over a
wide range of benchmarks. Our approach is particularly of interest when applied
to classifiers trained with novel loss functions, e.g., the recently-proposed
PolyLoss, where it differs substantially from (and outperforms) an
entropy-based loss. Further, we show that our approach can also be interpreted
as a kind of self-training using a very specific soft label, which we refer to
as the conjugate pseudolabel. Overall, our method provides a broad framework
for better understanding and improving test-time adaptation. Code is available
at this https URL

本文研究了针对分布漂移进行测试时间自适应（TTA）的方法，在各种函数类中元学习 TTA loss 能够迅速获得与熵函数类似的 loss 函数，并基于最优 loss 函数提供了更好的 TTA 方法。同时，在新型的 supervised training loss 函数中，我们的方法也显示了优异的表现，为改善测试时间自适应提供了广阔的框架。

共轭伪标签测试时调整

Test-Time Adaptation via Conjugate Pseudo-labels

Cross-entropy loss and focal loss are the most common choices when training
deep neural networks for classification problems. Generally speaking, however,
a good loss function can take on much more flexible forms, and should be
tailored for different tasks and datasets. Motivated by how functions can be
approximated via Taylor expansion, we propose a simple framework, named
PolyLoss, to view and design loss functions as a linear combination of
polynomial functions. Our PolyLoss allows the importance of different
polynomial bases to be easily adjusted depending on the targeting tasks and
datasets, while naturally subsuming the aforementioned cross-entropy loss and
focal loss as special cases. Extensive experimental results show that the
optimal choice within the PolyLoss is indeed dependent on the task and dataset.
Simply by introducing one extra hyperparameter and adding one line of code, our
Poly-1 formulation outperforms the cross-entropy loss and focal loss on 2D
image classification, instance segmentation, object detection, and 3D object
detection tasks, sometimes by a large margin.

提出一种名为 PolyLoss 的框架，将损失函数设计为多项式函数的线性组合，可根据任务和数据集灵活调整实现更优的分类效果。