仅使用单视2D照片的收藏集对3D感知生成对抗网络（GAN）的无监督学习最近取得了很多进展。然而，这些3D gan尚未证明人体，并且现有框架的产生的辐射场不是直接编辑的，从而限制了它们在下游任务中的适用性。我们通过开发一个3D GAN框架来解决这些挑战的解决方案，该框架学会在规范的姿势中生成人体或面部的辐射场，并使用显式变形场将其扭曲成所需的身体姿势或面部表达。使用我们的框架，我们展示了人体的第一个高质量的辐射现场生成结果。此外，我们表明，与未接受明确变形训练的3D GAN相比，在编辑其姿势或面部表情时，我们的变形感知训练程序可显着提高产生的身体或面部的质量。

使用单视图2D照片仅集合，无监督的高质量多视图 - 一致的图像和3D形状一直是一个长期存在的挑战。现有的3D GAN是计算密集型的，也是没有3D-一致的近似;前者限制了所生成的图像的质量和分辨率，并且后者对多视图一致性和形状质量产生不利影响。在这项工作中，我们提高了3D GAN的计算效率和图像质量，而无需依赖这些近似。为此目的，我们介绍了一种表现力的混合明确隐式网络架构，与其他设计选择一起，不仅可以实时合成高分辨率多视图一致图像，而且还产生高质量的3D几何形状。通过解耦特征生成和神经渲染，我们的框架能够利用最先进的2D CNN生成器，例如Stylega2，并继承它们的效率和表现力。在其他实验中，我们展示了与FFHQ和AFHQ猫的最先进的3D感知合成。

We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are not multi-view consistent; second, they often depend upon representation network architectures that are not expressive enough, and their results thus lack in image quality. We propose a novel generative model, named Periodic Implicit Generative Adversarial Networks (π-GAN or pi-GAN), for high-quality 3D-aware image synthesis. π-GAN leverages neural representations with periodic activation functions and volumetric rendering to represent scenes as view-consistent radiance fields. The proposed approach obtains state-of-the-art results for 3D-aware image synthesis with multiple real and synthetic datasets.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications. In practice, VAEs usually require multiple training rounds to choose the amount of information the latent variable should retain. This trade-off between the reconstruction error (distortion) and the KL divergence (rate) is typically parameterized by a hyperparameter $\beta$. In this paper, we introduce Multi-Rate VAE (MR-VAE), a computationally efficient framework for learning optimal parameters corresponding to various $\beta$ in a single training run. The key idea is to explicitly formulate a response function that maps $\beta$ to the optimal parameters using hypernetworks. MR-VAEs construct a compact response hypernetwork where the pre-activations are conditionally gated based on $\beta$. We justify the proposed architecture by analyzing linear VAEs and showing that it can represent response functions exactly for linear VAEs. With the learned hypernetwork, MR-VAEs can construct the rate-distortion curve without additional training and can be deployed with significantly less hyperparameter tuning. Empirically, our approach is competitive and often exceeds the performance of multiple $\beta$-VAEs training with minimal computation and memory overheads.

Diffusion models have emerged as the state-of-the-art for image generation, among other tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural fields. Our approach pre-processes training data, such as ShapeNet meshes, by converting them to continuous occupancy fields and factoring them into a set of axis-aligned triplane feature representations. Thus, our 3D training scenes are all represented by 2D feature planes, and we can directly train existing 2D diffusion models on these representations to generate 3D neural fields with high quality and diversity, outperforming alternative approaches to 3D-aware generation. Our approach requires essential modifications to existing triplane factorization pipelines to make the resulting features easy to learn for the diffusion model. We demonstrate state-of-the-art results on 3D generation on several object classes from ShapeNet.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

自动情绪识别（ER）最近由于其在许多实际应用中的潜力而引起了很多兴趣。在这种情况下，已经证明多模式方法可以通过结合多样化和互补的信息来源，从而提高性能（超过单峰方法），从而为嘈杂和缺失的方式提供了一些鲁棒性。在本文中，我们根据从视频中提取的面部和声音方式融合的尺寸ER专注于尺寸，其中探索了互补的视听（A-V）关系，以预测个人在价值空间中的情绪状态。大多数最先进的融合技术都依赖于反复的网络或常规的注意机制，这些机制无法有效利用A-V模式的互补性。为了解决这个问题，我们引入了A-V融合的联合跨注意模型，该模型在A-V模态上提取显着特征，从而可以有效利用模式间关系，同时保留模式内关系。特别是，它根据联合特征表示与单个模式的相关性计算交叉意义权重。通过将联合A-V特征表示形式部署到交叉意见模块中，它有助于同时利用内模式和模态关系，从而显着改善系统的性能，而不是香草交叉意见模块。我们提出的方法的有效性是在Recola和AffWild2数据集的挑战性视频中通过实验验证的。结果表明，我们的跨注意A-V融合模型提供了一种具有成本效益的解决方案，即使模式是嘈杂或不存在的，也可以超越最先进的方法。

基于自我监督的基于学习的预科可以使用小标签的数据集开发可靠和广义的深度学习模型，从而减轻了标签生成的负担。本文旨在评估基于CL的预处理对可转介的性能与非转介糖尿病性视网膜病（DR）分类的影响。我们已经开发了一个基于CL的框架，具有神经风格转移（NST）增强，以生成具有更好表示和初始化的模型，以检测颜色底面图像中的DR。我们将CL预估计的模型性能与用成像网权重预测的两个最先进的基线模型进行了比较。我们通过减少标记的训练数据（降至10％）进一步研究模型性能，以测试使用小标签数据集训练模型的鲁棒性。该模型在EYEPACS数据集上进行了培训和验证，并根据芝加哥伊利诺伊大学（UIC）的临床数据进行了独立测试。与基线模型相比，我们的CL预处理的基础网模型具有更高的AUC（CI）值（0.91（0.898至0.930），在UIC数据上为0.80（0.783至0.820）和0.83（0.783至0.820）（0.801至0.853）。在10％标记的培训数据时，在UIC数据集上测试时，基线模型中的FoldusNet AUC为0.81（0.78至0.84），比0.58（0.56至0.64）和0.63（0.56至0.64）和0.63（0.60至0.66）。基于CL的NST预处理可显着提高DL分类性能，帮助模型良好（可从Eyepacs转移到UIC数据），并允许使用小的带注释的数据集进行培训，从而减少临床医生的地面真相注释负担。

基于变压器的模型的出现，机器翻译已经快速发展。这些模型没有内置的明确的语言结构，但是它们仍然可以通过参与相关令牌隐式学习结构化的关系。我们假设通过明确赋予变形金刚具有结构性偏见，可以使这种结构学习变得更加健壮，我们研究了两种在这种偏见中构建的方法。一种方法，即TP变换器，可以增强传统的变压器体系结构，包括代表结构的附加组件。第二种方法通过将数据分割为形态令牌化来灌输数据级别的结构。我们测试了这些方法从英语翻译成土耳其语和Inuktitut的形态丰富的语言，并考虑自动指标和人类评估。我们发现，这两种方法中每种方法都允许网络实现更好的性能，但是此改进取决于数据集的大小。总而言之，结构编码方法使变压器更具样本效率，从而使它们能够从少量数据中表现得更好。