由于在不良视觉条件下记录的图像的密集像素级语义注释缺乏，因此对此类图像的语义分割的无监督域适应性（UDA）引起了兴趣。 UDA适应了在正常条件下训练的模型，以适应目标不利条件域。同时，多个带有驾驶场景的数据集提供了跨多个条件的相同场景的相应图像，这可以用作域适应的弱监督。我们提出了重新设计，这是对基于自训练的UDA方法的通用扩展，该方法利用了这些跨域对应关系。重新调整由两个步骤组成：（1）使用不确定性意识到的密度匹配网络将正常条件图像与相应的不良条件图像对齐，以及（2）使用自适应标签校正机制来完善不良预测，并使用正常预测。我们设计自定义模块，以简化这两个步骤，并在几个不良条件基准（包括ACDC和Dark Zurich）上设置域自适应语义分割的新技术。该方法不引入额外的训练参数，只有在训练期间最少的计算开销 - 可以用作撤离扩展，以改善任何给定的基于自我训练的UDA方法。代码可从https://github.com/brdav/refign获得。

尽管学术和公司的努力很大，但在不利视觉条件下的自动驾驶仍然证明具有挑战性。随着神经形态技术的成熟，其应用于机器人技术，自动驾驶汽车系统已成为积极研究的领域。低光和潜伏期的情况可以受益。为了使事件摄像机能够在感知任务中与LiDAR这样的主食传感器一起操作，我们提出了事件摄像机和激光镜头之间的直接，时间耦合的校准方法。利用事件摄像机的高动态范围和低光操作直接注册LIDAR激光返回，从而允许基于信息的相关方法优化两个传感器之间的6DOF外部校准。本文介绍了事件摄像机和激光镜头之间的第一种直接校准方法，从而消除了对基于框架的相机中介机构和/或高度准确的手部测量的依赖性。代码将公开可用。

除标准摄像机外，自动驾驶汽车通常还包括多个其他传感器，例如激光雷达和雷达，这些传感器有助于获取更丰富的信息以感知驾驶场景的内容。尽管最近的几项作品着重于通过使用特定于检查设置的架构组件融合某些传感器，例如相机，镜头或相机和雷达，但文献中缺少了通用和模块化传感器融合体系结构。在这项工作中，我们专注于2D对象检测，这是在2D图像域上定义的基本高级任务，并提出了HRFUSER，这是一种多分辨率的传感器融合体系结构，可直接扩展到任意数量的输入模式。 HRFUSER的设计基于用于仅图像密集预测的最新高分辨率网络，并结合了一种新型的多窗口交叉注意区块，作为在多种分辨率下进行多种模态融合的手段。即使单独的相机为2D检测提供了非常有用的功能，我们通过对Nuscenes的广泛实验进行了证明，并通过FOG数据集查看，我们的模型有效地利用了其他模态的互补功能，从而实质上改善了相机性能，并始终如一地超过了更胜过摄影机的状态表现。在正常情况下和不利条件下，用于2D检测的ART融合方法。源代码将公开可用。

由于长时间曝光时间，传统的基于帧的相机不可避免地遭受运动模糊。作为一种生物启发相机，事件摄像机以具有高时间分辨率的异步方式记录强度变化，在曝光时间内提供有效的图像劣化信息。在本文中，我们重新思考基于事件的图像去掩盖问题并将其展开成为端到端的两级图像恢复网络。为了有效地利用事件信息，我们设计（i）专门用于图像去纹理的新型对称累积事件表示，以及（ii）在我们网络的多个级别应用的仿射事件图像融合模块。我们还提出了网络的两个阶段之间的事件掩码所连接的连接，以避免信息丢失。在数据集级别，为了促进基于事件的运动解训，并促进挑战真实世界图像的评估，我们介绍了在照明控制的光学实验室中使用活动摄像机捕获的高质量模糊（HQBLUR）数据集。我们的多尺度事件融合网络（MEFNET）设置了用于运动解训的新技术，超越了先前最佳的基于图像的方法和GoPro上的公共实现的所有基于事件的方法（高达2.38dB即使在极端模糊条件下，也是HQBLUR Datasets。源代码和数据集将公开可用。

5级自动驾驶汽车自主权需要一个强大的视觉感知系统，可以在任何视觉条件下解析输入图像。但是，现有的语义分段数据集是由正常条件下捕获的图像主导，或者规模小。为了解决这个问题，我们引入了ACDC，具有对应于培训和测试原种视觉条件的语义分段方法的不利条件数据集。 ACDC由一组大型4006个图像组成，它在四个常见的不利条件之间同样分布：雾，夜间，雨和雪。每个不利条件图像具有高质量的细像素级语义注释，在正常条件下采取的相同场景的相应图像，以及区分清晰和不确定的语义内容的图像内区域之间的二进制掩模。因此，ACDC支持标准语义分割，新引入的不确定性感知语义分割。详细的实证研究表明，ACDC对最先进的监督和无人监督和无监督的方法的挑战，并表明了我们数据集在转向该领域的进展方面的价值。我们的数据集和基准是公开可用的。

Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch will lead to a significant performance drop. In this work, we aim to improve the cross-domain robustness of object detection. We tackle the domain shift on two levels: 1) the image-level shift, such as image style, illumination, etc., and 2) the instance-level shift, such as object appearance, size, etc. We build our approach based on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy. The two domain adaptation components are based on H-divergence theory, and are implemented by learning a domain classifier in adversarial training manner. The domain classifiers on different levels are further reinforced with a consistency regularization to learn a domain-invariant region proposal network (RPN) in the Faster R-CNN model. We evaluate our newly proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K, etc. The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.

Given a large graph with few node labels, how can we (a) identify the mixed network-effect of the graph and (b) predict the unknown labels accurately and efficiently? This work proposes Network Effect Analysis (NEA) and UltraProp, which are based on two insights: (a) the network-effect (NE) insight: a graph can exhibit not only one of homophily and heterophily, but also both or none in a label-wise manner, and (b) the neighbor-differentiation (ND) insight: neighbors have different degrees of influence on the target node based on the strength of connections. NEA provides a statistical test to check whether a graph exhibits network-effect or not, and surprisingly discovers the absence of NE in many real-world graphs known to have heterophily. UltraProp solves the node classification problem with notable advantages: (a) Accurate, thanks to the network-effect (NE) and neighbor-differentiation (ND) insights; (b) Explainable, precisely estimating the compatibility matrix; (c) Scalable, being linear with the input size and handling graphs with millions of nodes; and (d) Principled, with closed-form formula and theoretical guarantee. Applied on eight real-world graph datasets, UltraProp outperforms top competitors in terms of accuracy and run time, requiring only stock CPU servers. On a large real-world graph with 1.6M nodes and 22.3M edges, UltraProp achieves more than 9 times speedup (12 minutes vs. 2 hours) compared to most competitors.

High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods, which learn from automatically generated labels has shown great success on natural images, offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which may be caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a novel approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, which leads to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks - such as distinguishing treatments and mode of action.

While risk-neutral reinforcement learning has shown experimental success in a number of applications, it is well-known to be non-robust with respect to noise and perturbations in the parameters of the system. For this reason, risk-sensitive reinforcement learning algorithms have been studied to introduce robustness and sample efficiency, and lead to better real-life performance. In this work, we introduce new model-free risk-sensitive reinforcement learning algorithms as variations of widely-used Policy Gradient algorithms with similar implementation properties. In particular, we study the effect of exponential criteria on the risk-sensitivity of the policy of a reinforcement learning agent, and develop variants of the Monte Carlo Policy Gradient algorithm and the online (temporal-difference) Actor-Critic algorithm. Analytical results showcase that the use of exponential criteria generalize commonly used ad-hoc regularization approaches. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments.

Hierarchical learning algorithms that gradually approximate a solution to a data-driven optimization problem are essential to decision-making systems, especially under limitations on time and computational resources. In this study, we introduce a general-purpose hierarchical learning architecture that is based on the progressive partitioning of a possibly multi-resolution data space. The optimal partition is gradually approximated by solving a sequence of optimization sub-problems that yield a sequence of partitions with increasing number of subsets. We show that the solution of each optimization problem can be estimated online using gradient-free stochastic approximation updates. As a consequence, a function approximation problem can be defined within each subset of the partition and solved using the theory of two-timescale stochastic approximation algorithms. This simulates an annealing process and defines a robust and interpretable heuristic method to gradually increase the complexity of the learning architecture in a task-agnostic manner, giving emphasis to regions of the data space that are considered more important according to a predefined criterion. Finally, by imposing a tree structure in the progression of the partitions, we provide a means to incorporate potential multi-resolution structure of the data space into this approach, significantly reducing its complexity, while introducing hierarchical feature extraction properties similar to certain classes of deep learning architectures. Asymptotic convergence analysis and experimental results are provided for clustering, classification, and regression problems.