We propose UAD, a method for vision-based end-to-end autonomous driving
(E2EAD), achieving the best open-loop evaluation performance in nuScenes,
meanwhile showing robust closed-loop driving quality in CARLA. Our motivation
stems from the observation that current E2EAD models still mimic the modular
architecture in typical driving stacks, with carefully designed supervised
perception and prediction subtasks to provide environment information for
oriented planning. Although achieving groundbreaking progress, such design has
certain drawbacks: 1) preceding subtasks require massive high-quality 3D
annotations as supervision, posing a significant impediment to scaling the
training data; 2) each submodule entails substantial computation overhead in
both training and inference. To this end, we propose UAD, an E2EAD framework
with an unsupervised proxy to address all these issues. Firstly, we design a
novel Angular Perception Pretext to eliminate the annotation requirement. The
pretext models the driving scene by predicting the angular-wise spatial
objectness and temporal dynamics, without manual annotation. Secondly, a
self-supervised training strategy, which learns the consistency of the
predicted trajectories under different augment views, is proposed to enhance
the planning robustness in steering scenarios. Our UAD achieves 38.7% relative
improvements over UniAD on the average collision rate in nuScenes and surpasses
VAD for 41.32 points on the driving score in CARLA's Town05 Long benchmark.
Moreover, the proposed method only consumes 44.3% training resources of UniAD
and runs 3.4 times faster in inference. Our innovative design not only for the
first time demonstrates unarguable performance advantages over supervised
counterparts, but also enjoys unprecedented efficiency in data, training, and
inference. Code and models will be released at
this https URL

我们提出了 UAD，一种基于视觉的端到端自动驾驶 (E2EAD) 方法，在 nuScenes 中实现了最佳的开环评估性能，同时在 CARLA 中显示出强大的闭环驾驶质量。

无需昂贵模块化和 3D 手动标注的端到端自动驾驶

End-to-End Autonomous Driving without Costly Modularization and 3D  Manual Annotation

Early detection of anomalies in medical images such as brain MRI is highly
relevant for diagnosis and treatment of many conditions. Supervised machine
learning methods are limited to a small number of pathologies where there is
good availability of labeled data. In contrast, unsupervised anomaly detection
(UAD) has the potential to identify a broader spectrum of anomalies by spotting
deviations from normal patterns. Our research demonstrates that existing
state-of-the-art UAD approaches do not generalise well to diverse types of
anomalies in realistic multi-modal MR data. To overcome this, we introduce a
new UAD method named Aggregated Normative Diffusion (ANDi). ANDi operates by
aggregating differences between predicted denoising steps and ground truth
backwards transitions in Denoising Diffusion Probabilistic Models (DDPMs) that
have been trained on pyramidal Gaussian noise. We validate ANDi against three
recent UAD baselines, and across three diverse brain MRI datasets. We show that
ANDi, in some cases, substantially surpasses these baselines and shows
increased robustness to varying types of anomalies. Particularly in detecting
multiple sclerosis (MS) lesions, ANDi achieves improvements of up to 178% in
terms of AUPRC.

我们介绍了一种名为 ANDi 的新型无监督异常检测（UAD）方法，通过聚合预测去噪步骤与基于金字塔高斯噪声训练的去噪扩散概率模型（DDPMs）中的真实反向过渡之间的差异，在多种类型的异常中展现了显著优越性。

无监督异常检测：使用聚合的规范扩散

Unsupervised Anomaly Detection using Aggregated Normative Diffusion

Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize
anomalies without relying on annotations, only leveraging a model trained on a
dataset known to be free of anomalies. Diffusion models learn to modify inputs
$x$ to increase the probability of it belonging to a desired distribution,
i.e., they model the score function $\nabla_x \log p(x)$. Such a score function
is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a
pixel-wise anomaly score. However, diffusion models are trained to invert a
corruption process based on Gaussian noise and the learned score function is
unlikely to generalize to medical anomalies. This work addresses the problem of
how to learn a score function relevant for UAD and proposes DISYRE:
Diffusion-Inspired SYnthetic REstoration. We retain the diffusion-like pipeline
but replace the Gaussian noise corruption with a gradual, synthetic anomaly
corruption so the learned score function generalizes to medical, naturally
occurring anomalies. We evaluate DISYRE on three common Brain MRI UAD
benchmarks and substantially outperform other methods in two out of the three
tasks.

通过使用 Diffusion-Inspired SYnthetic REstoration（DISYRE）方法，学习适用于无监督异常检测（UAD）的评分函数，该方法使用逐渐引入的合成异常损坏代替了高斯噪声来模拟医学自然发生的异常，实验证明 DISYRE 在三个常见脑部 MRI UAD 基准中在两个任务中显著优于其他方法。

DISYRE: 无监督异常检测中的扩散激发的合成修复

DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised  Anomaly Detection

Since many safety-critical systems, such as surgical robots and autonomous
driving cars operate in unstable environments with sensor noise and incomplete
data, it is desirable for object detectors to take the localization uncertainty
into account. However, there are several limitations of the existing
uncertainty estimation methods for anchor-based object detection. 1) They model
the uncertainty of the heterogeneous object properties with different
characteristics and scales, such as location (center point) and scale (width,
height), which could be difficult to estimate. 2) They model box offsets as
Gaussian distributions, which is not compatible with the ground truth bounding
boxes that follow the Dirac delta distribution. 3) Since anchor-based methods
are sensitive to anchor hyper-parameters, their localization uncertainty could
also be highly sensitive to the choice of hyper-parameters. To tackle these
limitations, we propose a new localization uncertainty estimation method called
UAD for anchor-free object detection. Our method captures the uncertainty in
four directions of box offsets (left, right, top, bottom) that are homogeneous,
so that it can tell which direction is uncertain, and provide a quantitative
value of uncertainty in [0, 1]. To enable such uncertainty estimation, we
design a new uncertainty loss, negative power log-likelihood loss, to measure
the localization uncertainty by weighting the likelihood loss by its IoU, which
alleviates the model misspecification problem. Furthermore, we propose an
uncertainty-aware focal loss for reflecting the estimated uncertainty to the
classification score. Experimental results on COCO datasets demonstrate that
our method significantly improves FCOS, by up to 1.8 points, without
sacrificing computational efficiency.

本文提出了一种新的 UAD 方法，用于解决锚点法中现有不确定性评估方法的限制，通过在框偏移的四个方向（左、右、上、下）中捕获不确定性，一定程度上解决了现有方法中对异构目标性质的建模不准确、对误差的估计不准确等问题。