基于事件的视觉传感器基于视觉场景的变化产生具有高时间分辨率的异步事件流。随着事件的生成，这些传感器的特性允许精确快速地计算光学流量。对于从事件数据计算光学流的现有解决方案未能由于孔径问题而无法捕获真正的运动方向，请勿使用传感器的高时间分辨率，或者在嵌入式平台上实时运行太昂贵。在这项研究中，我们首先提供了我们之前的算法，武器（光圈稳健的多尺度流）的更快版本。新的优化软件版本（农场）显着提高了传统CPU的吞吐量。此外，我们呈现危害，一种农场算法的硬件实现，允许实时计算低功耗，嵌入式平台上的真实流量。建议的危害架构针对混合系统的片上器件，旨在最大限度地提高可配置性和吞吐量。硬件架构和农场算法是用异步的神经形态处理而开发的，放弃了事件帧的常用使用，而是仅使用不同事件的小历史运行，允许独立于传感器分辨率进行缩放。与现有方法相比，处理范例的这种变化将流量方向的估计变为高达73％，并在选择的基准配置上显示出危害最高为1.21 Mevent / s的危害。此吞吐量使实时性能能够实现迄今为止迄今为止最快速的基于活动的事件的光流的实现。

Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration. Often, SAR coordination strategies are manually designed by human experts who can remotely control the multi-robot system and enable semi-autonomous operations. However, in remote environments where connectivity is limited and human intervention is often not possible, decentralized collaboration strategies are needed for fully-autonomous operations. Nevertheless, decentralized coordination may be ineffective in adversarial environments due to sensor noise, actuation faults, or manipulation of inter-agent communication data. In this paper, we propose an algorithmic approach based on adversarial multi-agent reinforcement learning (MARL) that allows robots to efficiently coordinate their strategies in the presence of adversarial inter-agent communications. In our setup, the objective of the multi-robot team is to discover targets strategically in an obstacle-strewn geographical area by minimizing the average time needed to find the targets. It is assumed that the robots have no prior knowledge of the target locations, and they can interact with only a subset of neighboring robots at any time. Based on the centralized training with decentralized execution (CTDE) paradigm in MARL, we utilize a hierarchical meta-learning framework to learn dynamic team-coordination modalities and discover emergent team behavior under complex cooperative-competitive scenarios. The effectiveness of our approach is demonstrated on a collection of prototype grid-world environments with different specifications of benign and adversarial agents, target locations, and agent rewards.

We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary order. Until now these requirements of continual release and user-level privacy were considered in isolation. But, in practice, both these requirements come together as the users often contribute data repeatedly and multiple queries are made. We provide an algorithm that outputs a mean estimate at every time instant $t$ such that the overall release is user-level $\varepsilon$-DP and has the following error guarantee: Denoting by $M_t$ the maximum number of samples contributed by a user, as long as $\tilde{\Omega}(1/\varepsilon)$ users have $M_t/2$ samples each, the error at time $t$ is $\tilde{O}(1/\sqrt{t}+\sqrt{M}_t/t\varepsilon)$. This is a universal error guarantee which is valid for all arrival patterns of the users. Furthermore, it (almost) matches the existing lower bounds for the single-release setting at all time instants when users have contributed equal number of samples.

This work introduces the novel task of Source-free Multi-target Domain Adaptation and proposes adaptation framework comprising of \textbf{Co}nsistency with \textbf{N}uclear-Norm Maximization and \textbf{Mix}Up knowledge distillation (\textit{CoNMix}) as a solution to this problem. The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing. The source-free approach leverages target pseudo labels, which can be noisy, to improve the target adaptation. We introduce consistency between label preserving augmentations and utilize pseudo label refinement methods to reduce noisy pseudo labels. Further, we propose novel MixUp Knowledge Distillation (MKD) for better generalization on multiple target domains using various source-free STDA models. We also show that the Vision Transformer (VT) backbone gives better feature representation with improved domain transferability and class discriminability. Our proposed framework achieves the state-of-the-art (SOTA) results in various paradigms of source-free STDA and MTDA settings on popular domain adaptation datasets like Office-Home, Office-Caltech, and DomainNet. Project Page: https://sites.google.com/view/conmix-vcl

With the rising adoption of Machine Learning across the domains like banking, pharmaceutical, ed-tech, etc, it has become utmost important to adopt responsible AI methods to ensure models are not unfairly discriminating against any group. Given the lack of clean training data, generative adversarial techniques are preferred to generate synthetic data with several state-of-the-art architectures readily available across various domains from unstructured data such as text, images to structured datasets modelling fraud detection and many more. These techniques overcome several challenges such as class imbalance, limited training data, restricted access to data due to privacy issues. Existing work focusing on generating fair data either works for a certain GAN architecture or is very difficult to tune across the GANs. In this paper, we propose a pipeline to generate fairer synthetic data independent of the GAN architecture. The proposed paper utilizes a pre-processing algorithm to identify and remove bias inducing samples. In particular, we claim that while generating synthetic data most GANs amplify bias present in the training data but by removing these bias inducing samples, GANs essentially focuses more on real informative samples. Our experimental evaluation on two open-source datasets demonstrates how the proposed pipeline is generating fair data along with improved performance in some cases.

草图在快速执行的徒手绘图时会形成直观而有力的视觉表达。我们提出了一种从场景草图中综合现实照片的方法。不需要草图和照片对，我们的框架直接以无监督的方式从随时可用的大型照片数据集中学习。为此，我们引入了一个标准化模块，该模块在训练期间通过将照片和草图转换为标准化域，即边缘地图，从而提供伪素描 - 光谱对。草图和照片之间的域间隙减少也使我们可以将它们分为两个组成部分：整体场景结构和低级视觉样式，例如颜色和纹理。利用这一优势，我们通过结合草图的结构和参考照片的视觉样式来合成照片真实的图像。关于感知相似性指标和人类感知研究的广泛实验结果表明，该方法可以从场景草图和跑赢大于最先进的照片合成基准中产生逼真的照片。我们还证明，我们的框架通过编辑相应草图的笔触来促进对照片综合的可控操作，从而比依赖于区域级编辑的以前的方法提供了更多细粒度的细节。

具有输入序列长度的标准推理和基于变压器的体系结构的训练四倍。对于各种应用程序，尤其是在网页翻译，查询播放等方面，这非常大，因此，最近已经开发了几种方法来通过强制执行不同的注意力结构（例如稀疏性，低秩，使用内核）来加速注意计算。 。在这项工作中，我们将注意力计算视为最近的邻居检索的计算，并使用基于决策树的层次导航来降低每个查询令牌的检索成本，从线性序列长度从线性长度到几乎对数。基于这样的层次导航，我们设计了树形的树形，它可以使用两个有效的注意层之一 - TF - 注意和TC - 注意。 TF注意力以细粒的样式计算出注意力，而TC意见是一个粗糙的注意力层，它也确保梯度是“密集”的。为了优化此类具有挑战性的离散层，我们提出了一种两级自举训练方法。使用对标准NLP基准测试的广泛实验，尤其是对于长期序列，我们证明了我们的树形架构几乎可以像基线变压器一样准确，而注意力层则使用了30倍的失败。与Linform相比，在注意力层中使用类似的拖鞋时，准确性可能会高达12％。

灰度图像着色是AI在信息恢复中的引人入胜的应用。该问题的天生性质不良的性质使其更具挑战性，因为输出可能是多模式的。目前正在使用的基于学习的方法为直接情况产生可接受的结果，但在没有明确的图形分离的情况下通常无法恢复上下文信息。同样，由于在完整图像特征上训练的单个模型不足以学习各种数据模式，因此图像遭受了颜色出血和饱和背景。为了解决这些问题，我们提出了一个基于GAN的配色框架。在我们的方法中，每个量身定制的GAN管道都会使前景（使用对象级特征）或背景（使用全图像功能）着色。前景管道采用了一个具有自我注意事项的残留无UNET作为其发电机，使用了全图像功能和可可数据集中的相应对象级特征训练。背景管道依赖于该位置数据集的全图像功能和其他培训示例。我们设计了一个基于密集的融合网络，以通过基于特征的融合来获得最终的有色图像。我们显示了通常用于评估多模式问题（例如图像着色）并使用多个感知指标对我们的框架进行广泛的绩效评估的非感知评估指标的缺点。我们的方法的表现优于大多数基于学习的方法，并且产生的结果与最新的方法相当。此外，我们进行了运行时分析，并获得了每个图像的平均推理时间24ms。

大型预估计模型（例如GPT-3）取得了显着的性能，在训练过程中暴露于大量数据上。类似地，将如此大型模型提炼成紧凑的模型以进行有效的部署，也需要大量（标记或未标记的）培训数据。在本文中，我们提出了培训高质量紧凑型模型的教师指导培训（TGT）框架，该模型利用了预验证的生成模型获得的知识，同时避免了大量数据的需求。 TGT利用了教师获得基础数据域的良好表示的事实，该事实通常对应于比输入空间要低得多的尺寸歧管。此外，我们可以使用老师通过采样或基于梯度的方法来更有效地探索输入空间。因此，使TGT对于有限的数据或长尾设置特别有吸引力。我们正式在我们的概括范围内正式捕获了所提出的数据域探索的好处。我们发现TGT可以提高几个图像分类基准以及一系列文本分类和检索任务的准确性。

用于评估表结构识别算法的现有指标在捕获文本和空细胞对齐方面存在缺点。在本文中，我们以先前的工作为基础，并提出了一个新的度量标准的IOU相似性（TEDS（iou）），用于表结构识别，该识别使用边界框而不是文本，同时对上述缺点也是强大的。我们通过各种示例证明了对以前的度量标准的有效性。