在许多重要的科学和工程应用中发现了卷数据。渲染此数据以高质量和交互速率为苛刻的应用程序（例如虚拟现实）的可视化化，即使使用专业级硬件也无法实现。我们介绍了Fovolnet - 一种可显着提高数量数据可视化的性能的方法。我们开发了一种具有成本效益的渲染管道，该管道稀疏地对焦点进行了量度，并使用深层神经网络重建了全帧。 FOVEATED渲染是一种优先考虑用户焦点渲染计算的技术。这种方法利用人类视觉系统的属性，从而在用户视野的外围呈现数据时节省了计算资源。我们的重建网络结合了直接和内核预测方法，以产生快速，稳定和感知令人信服的输出。凭借纤细的设计和量化的使用，我们的方法在端到端框架时间和视觉质量中都优于最先进的神经重建技术。我们对系统的渲染性能，推理速度和感知属性进行了广泛的评估，并提供了与竞争神经图像重建技术的比较。我们的测试结果表明，Fovolnet始终在保持感知质量的同时，在传统渲染上节省了大量时间。

神经网络在压缩体积数据以进行科学可视化方面表现出巨大的潜力。但是，由于训练和推断的高成本，此类体积神经表示仅应用于离线数据处理和非交互式渲染。在本文中，我们证明，通过同时利用现代的GPU张量核心，本地CUDA神经网络框架以及在线培训，我们可以使用体积神经表示来实现高性能和高效率交互式射线追踪。此外，我们的方法是完全概括的，可以适应时变的数据集。我们提出了三种用于在线培训的策略，每种策略都利用GPU，CPU和核心流程技术的不同组合。我们还开发了三个渲染实现，允许交互式射线跟踪与实时卷解码，示例流和幕后神经网络推断相结合。我们证明，我们的体积神经表示可以扩展到Terascale，以进行常规网格体积可视化，并可以轻松地支持不规则的数据结构，例如OpenVDB，非结构化，AMR和粒子体积数据。

降低（DR）在高维数据的视觉分析中起着至关重要的作用。 DR的主要目的是揭示隐藏的模式，这些模式位于固有的低维歧管上。但是，当歧管被某些有影响力的数据属性严重扭曲或隐藏时，DR通常会忽略重要模式。本文介绍了一个功能学习框架FEALM，旨在为非线性DR生成优化的数据投影集，以便在隐藏的歧管中捕获重要模式。这些投影产生了最大不同的最近邻居图，因此由此产生的DR结果显着差异。为了获得这种功能，我们设计了一种优化算法，并引入了一种新的图形差异度量，称为邻居形状差异。此外，我们开发交互式可视化，以帮助比较获得的DR结果和每个DR结果的解释。我们通过使用合成数据集和对现实世界数据集的多个案例研究进行实验来证明FEALM的有效性。

扩散张量成像（DTI）已被用于研究神经退行性疾病对神经途径的影响，这可能导致这些疾病的更可靠和早期诊断，以及更好地了解它们如何影响大脑。我们介绍了一种基于标记为DTI光纤数据和相应统计数据的智能视觉分析系统，用于研究患者组。系统的AI增强界面通过组织和整体分析空间引导用户，包括统计特征空间，物理空间和不同组的患者的空间。我们使用自定义机器学习管道来帮助缩小此大型分析空间，然后通过一系列链接可视化务实拨动它。我们使用来自Parkinson进展标记倡议的研究数据库的实际数据进行多种案例研究。

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.