We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering. Motivated by the fact that informative point cloud features should be able to encode rich geometry and appearance cues and render realistic images, we train a point-cloud encoder within a devised point-based neural renderer by comparing the rendered images with real images on massive RGB-D data. The learned point-cloud encoder can be easily integrated into various downstream tasks, including not only high-level tasks like 3D detection and segmentation, but low-level tasks like 3D reconstruction and image synthesis. Extensive experiments on various tasks demonstrate the superiority of our approach compared to existing pre-training methods.

Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i.e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors. In particular, GNNs face greater challenges when both node features and graph structure are incomplete at the same time. The existing methods either focus on feature completion or structure completion. They usually rely on the matching relationship between features and structure, or employ joint learning of node representation and feature (or structure) completion in the hope of achieving mutual benefit. However, recent studies confirm that the mutual interference between features and structure leads to the degradation of GNN performance. When both features and structure are incomplete, the mismatch between features and structure caused by the missing randomness exacerbates the interference between the two, which may trigger incorrect completions that negatively affect node representation. To this end, in this paper we propose a general GNN framework based on teacher-student distillation to improve the performance of GNNs on incomplete graphs, namely T2-GNN. To avoid the interference between features and structure, we separately design feature-level and structure-level teacher models to provide targeted guidance for student model (base GNNs, such as GCN) through distillation. Then we design two personalized methods to obtain well-trained feature and structure teachers. To ensure that the knowledge of the teacher model is comprehensively and effectively distilled to the student model, we further propose a dual distillation mode to enable the student to acquire as much expert knowledge as possible.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are not readily used in relighting problems. To promote research in both fields, in this paper, we present UltraStage, a new 3D human dataset that contains more than 2K high-quality human assets captured under both multi-view and multi-illumination settings. Specifically, for each example, we provide 32 surrounding views illuminated with one white light and two gradient illuminations. In addition to regular multi-view images, gradient illuminations help recover detailed surface normal and spatially-varying material maps, enabling various relighting applications. Inspired by recent advances in neural representation, we further interpret each example into a neural human asset which allows novel view synthesis under arbitrary lighting conditions. We show our neural human assets can achieve extremely high capture performance and are capable of representing fine details such as facial wrinkles and cloth folds. We also validate UltraStage in single image relighting tasks, training neural networks with virtual relighted data from neural assets and demonstrating realistic rendering improvements over prior arts. UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks.

This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. Then, the object geometry can be recovered by solving a multi-view reconstruction problem. We devise an implicit neural representation-based method to solve the reconstruction problem and address the issues of imprecise hand pose estimation, relative hand-object motion, and insufficient geometry optimization for small objects. We also provide a newly collected dataset with 3D ground truth to validate the proposed approach.

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. Particularly, backpropagation through time (BPTT) with surrogate gradients (SG) is popularly used to achieve high performance in a very small number of time steps. However, it is at the cost of large memory consumption for training, lack of theoretical clarity for optimization, and inconsistency with the online property of biological learning and rules on neuromorphic hardware. Other works connect spike representations of SNNs with equivalent artificial neural network formulation and train SNNs by gradients from equivalent mappings to ensure descent directions. But they fail to achieve low latency and are also not online. In this work, we propose online training through time (OTTT) for SNNs, which is derived from BPTT to enable forward-in-time learning by tracking presynaptic activities and leveraging instantaneous loss and gradients. Meanwhile, we theoretically analyze and prove that gradients of OTTT can provide a similar descent direction for optimization as gradients based on spike representations under both feedforward and recurrent conditions. OTTT only requires constant training memory costs agnostic to time steps, avoiding the significant memory costs of BPTT for GPU training. Furthermore, the update rule of OTTT is in the form of three-factor Hebbian learning, which could pave a path for online on-chip learning. With OTTT, it is the first time that two mainstream supervised SNN training methods, BPTT with SG and spike representation-based training, are connected, and meanwhile in a biologically plausible form. Experiments on CIFAR-10, CIFAR-100, ImageNet, and CIFAR10-DVS demonstrate the superior performance of our method on large-scale static and neuromorphic datasets in small time steps.

神经网络（深度学习）是人工智能中的现代模型，并且在生存分析中已被利用。尽管以前的作品已经显示出一些改进，但培训出色的深度学习模型需要大量数据，这在实践中可能不存在。为了应对这一挑战，我们开发了一个基于Kullback-Leibler（KL）深度学习程序，以将外部生存预测模型与新收集的活动时间数据整合在一起。时间依赖性的KL歧视信息用于衡量外部数据和内部数据之间的差异。这是考虑使用先前信息来处理深度学习生存分析中的简短数据问题的第一项工作。仿真和实际数据结果表明，与以前的工作相比，所提出的模型可实现更好的性能和更高的鲁棒性。

Video-Text检索（VTR）是多模式理解的一项有吸引力但具有挑战性的任务，该任务旨在在给定查询（视频）的情况下搜索相关的视频（文本）。现有方法通常采用完全异构的视觉文本信息来对齐视频和文本，同时缺乏对这两种模式中均匀的高级语义信息的认识。为了填补这一差距，在这项工作中，我们提出了一个新颖的视觉语言对准模型，名为VTR Hise，该模型通过合并显式高级语义来改善跨模式的表示。首先，我们探讨了显式高级语义的层次结构属性，并将其进一步分为两个级别，即离散的语义和整体语义。具体来说，对于视觉分支，我们利用了现成的语义实体预测器来生成离散的高级语义。同时，采用训练有素的视频字幕模型来输出整体高级语义。至于文本方式，我们将文本分为三个部分，包括发生，动作和实体。特别是，这种情况对应于整体高级语义，同时动作和实体代表离散的语义。然后，利用不同的图推理技术来促进整体和离散的高级语义之间的相互作用。广泛的实验表明，借助明确的高级语义，我们的方法在包括MSR-VTT，MSVD和DIDEMO在内的三个基准数据集上实现了优于最先进方法的卓越性能。

以在线方式进行功能选择的在线流媒体特征选择（OSFS）在处理高维数据方面起着重要作用。在许多真实的应用程序（例如智能医疗平台）中，流媒体功能始终存在一些缺少的数据，这在进行OSFS（即如何在稀疏流式传输功能和标签之间建立不确定的关系）方面提出了至关重要的挑战。不幸的是，现有的OSFS算法从未考虑过这种不确定的关系。为了填补这一空白，我们在本文中提出了一个不确定性（OS2FSU）算法的在线稀疏流媒体特征选择。 OS2FSU由两个主要部分组成：1）潜在因素分析用于预测稀疏流特征中缺少的数据，然后使用划分功能选择，而2）使用模糊逻辑和邻里粗糙集来减轻估计流流之间的不确定性进行功能选择期间的功能和标签。在实验中，将OS2FSU与六个真实数据集中的五种最先进的OSFS算法进行了比较。结果表明，在OSF中遇到丢失的数据时，OS2FSU胜过其竞争对手。

基于图形卷积的方法已成功应用于同质图上的表示学习，其中具有相同标签或相似属性的节点往往相互连接。由于这些方法使用的图形卷积网络（GCN）的同义假设，它们不适合异质图，其中具有不同标记或不同属性的节点往往相邻。几种方法试图解决这个异质问题，但是它们没有改变GCN的基本聚合机制，因为它们依靠求和操作员来汇总邻近节点的信息，这隐含地遵守同质假设。在这里，我们介绍了一种新颖的聚合机制，并开发了基于随机步行聚集的图形神经网络（称为RAW-GNN）方法。提出的方法将随机步行策略与图神经网络集成在一起。新方法利用广度优先的随机步行搜索来捕获同质信息和深度优先搜索以收集异性信息。它用基于路径的社区取代了传统社区，并基于经常性神经网络引入了新的基于路径的聚合器。这些设计使RAW-GNN适用于同质图和异质图。广泛的实验结果表明，新方法在各种同质图和异质图上实现了最先进的性能。