Understanding the scene around the ego-vehicle is key to assisted and
autonomous driving. Nowadays, this is mostly conducted using cameras and laser
scanners, despite their reduced performances in adverse weather conditions.
Automotive radars are low-cost active sensors that measure properties of
surrounding objects, including their relative speed, and have the key advantage
of not being impacted by rain, snow or fog. However, they are seldom used for
scene understanding due to the size and complexity of radar raw data and the
lack of annotated datasets. Fortunately, recent open-sourced datasets have
opened up research on classification, object detection and semantic
segmentation with raw radar signals using end-to-end trainable models. In this
work, we propose several novel architectures, and their associated losses,
which analyse multiple "views" of the range-angle-Doppler radar tensor to
segment it semantically. Experiments conducted on the recent CARRADA dataset
demonstrate that our best model outperforms alternative models, derived either
from the semantic segmentation of natural images or from radar scene
understanding, while requiring significantly fewer parameters. Both our code
and trained models are available at this https URL

该研究通过使用汽车雷达等主动传感器，提出了基于端到端可训练模型的多视角分析方法，实现了场景的语义分割，进一步提高自动驾驶的效率和准确性。

多视角雷达语义分割

Multi-View Radar Semantic Segmentation

Recent developments in unsupervised representation learning have successfully
established the concept of transfer learning in NLP. Mainly three forces are
driving the improvements in this area of research: More elaborated
architectures are making better use of contextual information. Instead of
simply plugging in static pre-trained representations, these are learned based
on surrounding context in end-to-end trainable models with more intelligently
designed language modelling objectives. Along with this, larger corpora are
used as resources for pre-training large language models in a self-supervised
fashion which are afterwards fine-tuned on supervised tasks. Advances in
parallel computing as well as in cloud computing, made it possible to train
these models with growing capacities in the same or even in shorter time than
previously established models. These three developments agglomerate in new
state-of-the-art (SOTA) results being revealed in a higher and higher
frequency. It is not always obvious where these improvements originate from, as
it is not possible to completely disentangle the contributions of the three
driving forces. We set ourselves to providing a clear and concise overview on
several large pre-trained language models, which achieved SOTA results in the
last two years, with respect to their use of new architectures and resources.
We want to clarify for the reader where the differences between the models are
and we furthermore attempt to gain some insight into the single contributions
of lexical/computational improvements as well as of architectural changes. We
explicitly do not intend to quantify these contributions, but rather see our
work as an overview in order to identify potential starting points for
benchmark comparisons. Furthermore, we tentatively want to point at potential
possibilities for improvement in the field of open-sourcing and reproducible
research.

该文章介绍了近期在自然语言处理中提高模型性能所用的三种方法：更加复杂的语言模型、更大的语料库和并行计算，总结了一些最近两年新增的大型预训练语言模型，并探讨了新架构与资源所带来的影响，以此为起点提供了一些方便可复制的研究结果。