Ensuring safety in automated driving is a major challenge for the automotive
industry. Special attention is paid to artificial intelligence, in particular
to Deep Neural Networks (DNNs), which is considered a key technology in the
realization of highly automated driving. DNNs learn from training data, which
means that they only achieve good accuracy within the underlying data
distribution of the training data. When leaving the training domain, a
distributional shift is caused, which can lead to a drastic reduction of
accuracy. In this work, we present a proof of concept for a safety mechanism
that can detect the leaving of the domain online, i.e. at runtime. In our
experiments with the Synthia data set we can show that a 100 % correct
detection of whether the input data is inside or outside the domain is
achieved. The ability to detect when the vehicle leaves the domain can be an
important requirement for certification.

确保自动驾驶的安全性是汽车行业的主要挑战，特别关注人工智能，尤其是深度神经网络作为高度自动化驾驶实现的关键技术；本研究提出了一个在线安全机制的概念验证，能够检测数据是否遗漏训练领域，并在 Synthia 数据集上实验，显示百分之百正确检测输入数据是否在领域内；检测车辆离开领域的能力可能成为认证的重要要求。

自动驾驶在线域外检测

Online Out-of-Domain Detection for Automated Driving

Recent methods focus on learning a unified semantic-aligned visual
representation to transfer knowledge between two domains, while ignoring the
effect of semantic-free visual representation in alleviating the biased
recognition problem. In this paper, we propose a novel Domain-aware Visual Bias
Eliminating (DVBE) network that constructs two complementary visual
representations, i.e., semantic-free and semantic-aligned, to treat seen and
unseen domains separately. Specifically, we explore cross-attentive
second-order visual statistics to compact the semantic-free representation, and
design an adaptive margin Softmax to maximize inter-class divergences. Thus,
the semantic-free representation becomes discriminative enough to not only
predict seen class accurately but also filter out unseen images, i.e., domain
detection, based on the predicted class entropy. For unseen images, we
automatically search an optimal semantic-visual alignment architecture, rather
than manual designs, to predict unseen classes. With accurate domain detection,
the biased recognition problem towards the seen domain is significantly
reduced. Experiments on five benchmarks for classification and segmentation
show that DVBE outperforms existing methods by averaged 5.7% improvement.

该论文提出了一种新的基于领域感知的视觉偏见消除网络（DVBE），该网络构建了两种互补的视觉表示，即无语义和有语义。实验结果表明，DVBE 相比现有方法，平均提高了 5.7％的性能表现。

面向通用零样本学习的领域感知视觉偏差消除

Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning

In this paper we introduce domain detection as a new natural language
processing task. We argue that the ability to detect textual segments which are
domain-heavy, i.e., sentences or phrases which are representative of and
provide evidence for a given domain could enhance the robustness and
portability of various text classification applications. We propose an
encoder-detector framework for domain detection and bootstrap classifiers with
multiple instance learning (MIL). The model is hierarchically organized and
suited to multilabel classification. We demonstrate that despite learning with
minimal supervision, our model can be applied to text spans of different
granularities, languages, and genres. We also showcase the potential of domain
detection for text summarization.

本文提出了域检测作为一项新的自然语言处理任务，介绍了编码器 - 检测器框架结合多实例学习的域检测模型，并应用于不同粒度、语言和体裁的文本中，展示了域检测在文本摘要中的潜力。