We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning. We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (i) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on three video generation benchmarks, including the challenging Kinetics-600. (ii) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60x against autoregressive models. (iii) A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains. The source code and trained models will be released to the public at https://magvit.cs.cmu.edu.

以前的工作通常认为，改善卷积网络的空间不变性是对象计数的关键。但是，在验证了几个主流计数网络之后，我们出人意料地发现，太严格的像素级空间不变性将导致密度图生成中的噪声过高。在本文中，我们尝试使用本地连接的高斯内核来替换原始的卷积过滤器，以估计密度图中的空间位置。这样做的目的是允许特征提取过程潜在刺激密度生成过程以克服注释噪声。受到先前工作的启发，我们提出了一个低级别的近似值，并伴随着翻译不变性，以有利地实施大量高斯卷积的近似值。我们的工作指向了后续研究的新方向，该方向应该研究如何正确放松对象计数过于严格的像素级空间不变性。我们在4个主流对象计数网络（即MCNN，CSRNET，SANET和RESNET-50）上评估我们的方法。在7个流行的基准测试中进行了大量实验，用于3种应用（即人群，车辆和植物计数）。实验结果表明，我们的方法明显优于其他最先进的方法，并实现对物体空间位置的有希望的学习。

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To address this issue, this paper proposes Contrastive Adaptation Network (CAN) optimizing a new metric which explicitly models the intra-class domain discrepancy and the inter-class domain discrepancy. We design an alternating update strategy for training CAN in an end-to-end manner. Experiments on two real-world benchmarks Office-31 and VisDA-2017 demonstrate that CAN performs favorably against the state-of-the-art methods and produces more discriminative features.

Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants$\unicode{x2014}$what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world$\unicode{x2014}$also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing$\unicode{x2014}$leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first$\unicode{x2014}$and key$\unicode{x2014}$step towards such an ecology.

As an autonomous system performs a task, it should maintain a calibrated estimate of the probability that it will achieve the user's goal. If that probability falls below some desired level, it should alert the user so that appropriate interventions can be made. This paper considers settings where the user's goal is specified as a target interval for a real-valued performance summary, such as the cumulative reward, measured at a fixed horizon $H$. At each time $t \in \{0, \ldots, H-1\}$, our method produces a calibrated estimate of the probability that the final cumulative reward will fall within a user-specified target interval $[y^-,y^+].$ Using this estimate, the autonomous system can raise an alarm if the probability drops below a specified threshold. We compute the probability estimates by inverting conformal prediction. Our starting point is the Conformalized Quantile Regression (CQR) method of Romano et al., which applies split-conformal prediction to the results of quantile regression. CQR is not invertible, but by using the conditional cumulative distribution function (CDF) as the non-conformity measure, we show how to obtain an invertible modification that we call \textbf{P}robability-space \textbf{C}onformalized \textbf{Q}uantile \textbf{R}egression (PCQR). Like CQR, PCQR produces well-calibrated conditional prediction intervals with finite-sample marginal guarantees. By inverting PCQR, we obtain marginal guarantees for the probability that the cumulative reward of an autonomous system will fall within an arbitrary user-specified target intervals. Experiments on two domains confirm that these probabilities are well-calibrated.

在不失去先前学习的情况下学习新任务和技能（即灾难性遗忘）是人为和生物神经网络的计算挑战，但是人工系统努力与其生物学类似物达成平等。哺乳动物的大脑采用众多神经手术来支持睡眠期间的持续学习。这些是人工适应的成熟。在这里，我们研究了建模哺乳动物睡眠的三个不同组成部分如何影响人工神经网络中的持续学习：（1）在非比型眼运动（NREM）睡眠期间观察到的垂直记忆重播过程； （2）链接到REM睡眠的生成记忆重播过程； （3）已提出的突触降压过程，以调整信噪比和支持神经保养。在评估持续学习CIFAR-100图像分类基准上的性能时，我们发现将所有三个睡眠组件的包含在内。在以后的任务期间，训练和灾难性遗忘在训练过程中提高了最高准确性。尽管某些灾难性遗忘在网络培训过程中持续存在，但更高水平的突触缩减水平会导致更好地保留早期任务，并进一步促进随后培训期间早期任务准确性的恢复。一个关键的要点是，在考虑使用突触缩小范围的水平时，手头有一个权衡 - 更具侵略性的缩减更好地保护早期任务，但较少的缩减可以增强学习新任务的能力。中级水平可以在训练过程中与最高的总体精度达到平衡。总体而言，我们的结果都提供了有关如何适应睡眠组件以增强人工连续学习系统的洞察力，并突出了未来神经科学睡眠研究的领域，以进一步进一步进行此类系统。

扎根的情况识别（GSR）旨在生成图像的结构化语义摘要，以``类人''事件的理解。具体而言，GSR任务不仅检测出明显的活动动词（例如购买），而且还可以预测所有相应的语义角色（例如代理和商品）。受对象检测和图像字幕任务的启发，现有方法通常采用两个阶段框架：1）检测活动动词，然后2）基于检测到的动词来预测语义角色。显然，这个不合逻辑的框架构成了语义理解的巨大障碍。首先，仅没有语义角色的前检测动词不可避免地无法区分许多类似的日常活动（例如，提供和赠与，买卖）。其次，以封闭的自动回归方式预测语义角色几乎无法利用动词和角色之间的语义关系。为此，在本文中，我们提出了一个新颖的两阶段框架，该框架着重于在动词和角色中利用这种双向关系。在第一阶段，我们没有预测动词，而是推迟检测步骤并假设一个伪标记，其中每个相应的语义角色都从图像中学到了每个相应的语义角色的中间表示。在第二阶段，我们利用变压器层发掘动词和语义角色内的潜在语义关系。借助一组支持图像，替代学习方案旨在同时优化结果：使用与图像相对应的名词更新动词，并使用支持图像中的动词更新名词。关于挑战性SWIG基准测试的广泛实验结果表明，我们翻新的框架在各种指标下的表现优于其他最先进的方法。

引入后二十年多，退火重要性采样（AIS）仍然是边际可能性估计的最有效方法之一。它依赖于一系列分布序列在可聊天的初始分布和利益的目标分布之间插值，我们从大约使用非均匀的马尔可夫链中模拟了分布。为了获得边际可能性的重要性采样估计，AIS引入了扩展的目标分布，以重新持续马尔可夫链提案。尽管已经大量努力通过更改AIS使用的提案分布，通过更改中间分布和相应的马尔可夫内核，但不被评估的问题是AIS使用方便但次优的扩展目标分布。这可能会阻碍其性能。我们在这里利用基于分数的生成建模（SGM）的最新进展来近似与Langevin和Hamiltonian Dynamics离散化相对应的AIS建议的最佳扩展目标分布。我们在许多合成基准分布和变异自动编码器上展示了这些新颖的，可区分的AIS程序。

具有隐式函数的单视RGB-D人重建通常以每点分类为例。具体而言，首先将相机视图中的一组3D位置投影到图像上，并随后针对每个3D位置提取相应的功能。然后，每个3D位置的特征用于独立分类，无论相应的3D点在观察到的对象内还是外部。此过程导致了亚最佳结果，因为仅通过提取的特征隐式地考虑了相邻位置的预测之间的相关性。为了获得更准确的结果，我们提出了占用平面（OPLANES）表示，该表示可以使单视RGB-D人类重建作为对平面上的占用预测，这些预测切成摄像机的视图。这种表示比体素电网提供了更大的灵活性，并使比每点分类更好地利用相关性。在具有挑战性的S3D数据上，我们观察一个基于Oplanes表示的简单分类器，以产生引人注目的结果，尤其是在由于其他对象和部分可见性引起的部分遮挡的困难情况下，这尚未通过先前的工作解决。