我们研究自主代理如何学会从不同领域（例如不同环境或不同代理）中的示范中执行任务。这样的跨域模仿学习需要例如从人类专家的演示中培训人造代理。我们提出了一个可扩展的框架，该框架可以实现跨域模仿学习，而无需访问其他演示或进一步的领域知识。我们共同培训学习者的政策，并通过对抗性培训学习学习者和专家领域的映射。我们通过使用共同信息标准来找到包含与任务相关的信息的专家状态空间的嵌入，并且对域细节不变。此步骤大大简化了估计学习者和专家领域之间的映射，因此有助于端到端学习。我们证明了在相当不同的域之间成功转移了政策，而没有额外的示范，以及其他方法失败的情况。

部署到现实世界的自主智能代理必须与对感官输入的对抗性攻击保持强大的态度。在加强学习中的现有工作集中于最小值扰动攻击，这些攻击最初是为了模仿计算机视觉中感知不变性的概念。在本文中，我们注意到，这种最小值扰动攻击可以由受害者琐碎地检测到，因为这些导致观察序列与受害者的行为不符。此外，许多现实世界中的代理商（例如物理机器人）通常在人类主管下运行，这些代理商不容易受到这种扰动攻击的影响。结果，我们建议专注于幻觉攻击，这是一种与受害者的世界模式一致的新型攻击形式。我们为这个新颖的攻击框架提供了正式的定义，在各种条件下探索了其特征，并得出结论，代理必须寻求现实主义反馈以对幻觉攻击具有强大的态度。

我们提出了一种准确的3D重建方法的方法。我们基于神经重建和渲染（例如神经辐射场（NERF））的最新进展的优势。这种方法的一个主要缺点是，它们未能重建对象的任何部分，这些部分在训练图像中不明确可见，这通常是野外图像和视频的情况。当缺乏证据时，可以使用诸如对称的结构先验来完成缺失的信息。但是，在神经渲染中利用此类先验是高度不平凡的：虽然几何和非反射材料可能是对称的，但环境场景的阴影和反射通常不是对称的。为了解决这个问题，我们将软对称性约束应用于3D几何和材料特性，并将外观纳入照明，反照率和反射率。我们在最近引入的CO3D数据集上评估了我们的方法，这是由于重建高度反射材料的挑战，重点是汽车类别。我们表明，它可以用高保真度重建未观察到的区域，并渲染高质量的新型视图图像。

在这项工作中，我们调查如何实现方面，以纯粹来自数据的平台输入变换，而不会被赋予那些转换的模型。例如，卷积神经网络（CNNS）是对图像转换的等意识别，可以容易地建模的变换（通过垂直或水平地移动像素）。其他转换，例如外平面旋转，不承认一个简单的分析模型。我们提出了一种自动编码器架构，其嵌入了obeeys同时嵌入了一组任意的标准关系，例如翻译，旋转，颜色变化以及许多其他。这意味着它可以拍摄输入图像，并产生由之前未观察到的给定金额的版本（例如，相同对象的不同观点或颜色变化）。尽管延伸到许多（甚至是非几何）转换，但我们的模型在翻译标准规范的特殊情况下完全缩短了CNN。协调对深度网络的可解释性和稳健性是重要的，并且我们证明了在几个合成和实际数据集上成功重新渲染的输入图像的转换版本的结果，以及对象姿态估计的结果。

We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-the-art results in eight unsupervised clustering benchmarks spanning image classification and segmentation. These include STL10, an unsupervised variant of ImageNet, and CIFAR10, where we significantly beat the accuracy of our closest competitors by 6.6 and 9.5 absolute percentage points respectively. The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image. The trained network directly outputs semantic labels, rather than high dimensional representations that need external processing to be usable for semantic clustering. The objective is simply to maximise mutual information between the class assignments of each pair. It is easy to implement and rigorously grounded in information theory, meaning we effortlessly avoid degenerate solutions that other clustering methods are susceptible to. In addition to the fully unsupervised mode, we also test two semi-supervised settings. The first achieves 88.8% accuracy on STL10 classification, setting a new global state-of-the-art over all existing methods (whether supervised, semi-supervised or unsupervised). The second shows robustness to 90% reductions in label coverage, of relevance to applications that wish to make use of small amounts of labels. github.com/xu-ji/IIC

卷积神经网络（CNNS）非常有效，因为它们利用自然图像的固有转换不变性。但是，翻译只是无数的有用空间转换之一。在考虑其他空间的侵犯侵犯性时可以获得相同的效率吗？过去已经考虑过这种广义综合，但以高计算成本为例。我们展示了一个简单和精确的建筑，但标准卷积具有相同的计算复杂性。它由一个恒定的图像扭曲，后跟一个简单的卷积，这是深度学习工具箱中的标准块。通过精心制作的经线，所产生的架构可以使成功的架构成为各种各样的双参数空间转换。我们展示了令人鼓舞的现实情景结果，包括谷歌地球数据集（旋转和缩放）中车辆姿势的估计，并且面部在野外注释的面部地标中的面部姿势（在透视下的3D旋转）。

传统上通过在线学习物体的外观模型来解决任意对象跟踪问题，作为视频本身的唯一培训数据。尽管这些方法的成功，但他们的在线方法本身限制了他们可以学习的模型的丰富性。最近，已经进行了几次尝试利用深度卷积网络的表现力。然而，当预先知道对象轨道时，有必要在线执行随机梯度下降以调整网络的权重，严重影响系统的速度。本文在ILSVRC15数据集上装备了一种基本的跟踪算法，该历程在ILSVRC15数据集上训练了训练的端到端，用于视频中的对象检测。我们的跟踪器以超出实时的帧速率运行，尽管其极端简单，但在多个基准中实现了最先进的性能。

Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of $\mathbf{0.9535}$ and $\mathbf{0.9636}$ in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart.

Scene change detection is an image processing problem related to partitioning pixels of a digital image into foreground and background regions. Mostly, visual knowledge-based computer intelligent systems, like traffic monitoring, video surveillance, and anomaly detection, need to use change detection techniques. Amongst the most prominent detection methods, there are the learning-based ones, which besides sharing similar training and testing protocols, differ from each other in terms of their architecture design strategies. Such architecture design directly impacts on the quality of the detection results, and also in the device resources capacity, like memory. In this work, we propose a novel Multiscale Cascade Residual Convolutional Neural Network that integrates multiscale processing strategy through a Residual Processing Module, with a Segmentation Convolutional Neural Network. Experiments conducted on two different datasets support the effectiveness of the proposed approach, achieving average overall $\boldsymbol{F\text{-}measure}$ results of $\boldsymbol{0.9622}$ and $\boldsymbol{0.9664}$ over Change Detection 2014 and PetrobrasROUTES datasets respectively, besides comprising approximately eight times fewer parameters. Such obtained results place the proposed technique amongst the top four state-of-the-art scene change detection methods.

Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper proposes two novel deep learning-based architectures for image classification purposes, i.e., the Discriminant Deep Image Prior Network and the Discriminant Deep Image Prior Network+, which combine Deep Image Prior and Triplet Networks learning strategies. Experiments conducted over three well-known public remote sensing image datasets achieved state-of-the-art results, evidencing the effectiveness of using deep image priors for remote sensing image classification.