We introduce Situation Monitor, a novel zero-shot Out-of-Distribution (OOD)
detection approach for transformer-based object detection models to enhance
reliability in safety-critical machine learning applications such as autonomous
driving. The Situation Monitor utilizes the Diversity-based Budding Ensemble
Architecture (DBEA) and increases the OOD performance by integrating a
diversity loss into the training process on top of the budding ensemble
architecture, detecting Far-OOD samples and minimizing false positives on
Near-OOD samples. Moreover, utilizing the resulting DBEA increases the model's
OOD performance and improves the calibration of confidence scores, particularly
concerning the intersection over union of the detected objects. The DBEA model
achieves these advancements with a 14% reduction in trainable parameters
compared to the vanilla model. This signifies a substantial improvement in
efficiency without compromising the model's ability to detect OOD instances and
calibrate the confidence scores accurately.

引入了 Situation Monitor，一种用于增强自动驾驶等安全关键机器学习应用的基于 Transformer 的目标检测模型的零样本分布外（OOD）检测方法。该方法利用了多样性损失，通过集成架构，检测远样本和最小化接近样本的误报。此外，使用得到的 Diversity-based Budding Ensemble Architecture（DBEA）提高了模型的 OOD 性能，并改善了置信度得分的校准，特别是关于检测到的对象的交集。与普通模型相比，DBEA 模型减少了 14% 的可训练参数，从而在不影响模型检测 OOD 实例和准确校准置信度得分的能力的情况下，大大提高了效率。

局势监测：使用发展集合架构的多样性驱动零样本外分布检测实现物体检测

Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution  Detection using Budding Ensemble Architecture for Object Detection

Seasonal forecasting is a crucial task when it comes to detecting the extreme
heat and colds that occur due to climate change. Confidence in the predictions
should be reliable since a small increase in the temperatures in a year has a
big impact on the world. Calibration of the neural networks provides a way to
ensure our confidence in the predictions. However, calibrating regression
models is an under-researched topic, especially in forecasters. We calibrate a
UNet++ based architecture, which was shown to outperform physics-based models
in temperature anomalies. We show that with a slight trade-off between
prediction error and calibration error, it is possible to get more reliable and
sharper forecasts. We believe that calibration should be an important part of
safety-critical machine learning applications such as weather forecasters.

季节性预测是探测气候变化引起的极端炎热与寒冷的关键任务。我们应该对预测结果有可靠的信心，因为每年温度增加一点点都对世界产生巨大影响。神经网络的校准提供了确保预测可信度的方法，然而，在预测中校准回归模型是一个鲜为人知的课题。我们通过校准基于 UNet++ 架构的模型，证明相对于基于物理的模型在温度异常方面表现更优。我们发现，在预测误差和校准误差之间做一定权衡，可以得到更可靠、更精确的预测。我们相信，在天气预报等安全关键的机器学习应用中，校准应该是一个重要部分。