Kaidong Wang

Yitao Zhu, Sheng Wang, Mengjie Xu et al.

Multiple cameras can provide comprehensive multi-view video coverage of a person. Fusing this multi-view data is crucial for tasks like behavioral analysis, although it traditionally requires camera calibration, a process that is often complex. Moreover, previous studies have overlooked the challenges posed by self-occlusion under multiple views and the continuity of human body shape estimation. In this study, we introduce a method to reconstruct the 3D human body from multiple uncalibrated camera views. Initially, we utilize a pre-trained human body encoder to process each camera view individually, enabling the reconstruction of human body models and parameters for each view along with predicted camera positions. Rather than merely averaging the models across views, we develop a neural network trained to assign weights to individual views for all human body joints, based on the estimated distribution of joint distances from each camera. Additionally, we focus on the mesh surface of the human body for dynamic fusion, allowing for the seamless integration of facial expressions and body shape into a unified human body model. Our method has shown excellent performance in reconstructing the human body on two public datasets, advancing beyond previous work from the SMPL model to the SMPL-X model. This extension incorporates more complex hand poses and facial expressions, enhancing the detail and accuracy of the reconstructions. Crucially, it supports the flexible ad-hoc deployment of any number of cameras, offering significant potential for various applications. Our code is available at https://github.com/AbsterZhu/MUC.

Kaidong Wang, Yao Wang, Xiuwu Liao et al.

Graphs, depicting the interrelations between variables, has been widely used as effective side information for accurate data recovery in various matrix/tensor recovery related applications. In this paper, we study the tensor completion problem with graph information. Current research on graph-regularized tensor completion tends to be task-specific, lacking generality and systematic approaches. Furthermore, a recovery theory to ensure performance remains absent. Moreover, these approaches overlook the dynamic aspects of graphs, treating them as static akin to matrices, even though graphs could exhibit dynamism in tensor-related scenarios. To confront these challenges, we introduce a pioneering framework in this paper that systematically formulates a novel model, theory, and algorithm for solving the dynamic graph regularized tensor completion problem. For the model, we establish a rigorous mathematical representation of the dynamic graph, based on which we derive a new tensor-oriented graph smoothness regularization. By integrating this regularization into a tensor decomposition model based on transformed t-SVD, we develop a comprehensive model simultaneously capturing the low-rank and similarity structure of the tensor. In terms of theory, we showcase the alignment between the proposed graph smoothness regularization and a weighted tensor nuclear norm. Subsequently, we establish assurances of statistical consistency for our model, effectively bridging a gap in the theoretical examination of the problem involving tensor recovery with graph information. In terms of the algorithm, we develop a solution of high effectiveness, accompanied by a guaranteed convergence, to address the resulting model. To showcase the prowess of our proposed model in contrast to established ones, we provide in-depth numerical experiments encompassing synthetic data as well as real-world datasets.

Yiyang Yang, Xiejian Chi, Shanxing Gao et al.

Data quality is critical to Intelligent Transportation Systems (ITS), as complete and accurate traffic data underpin reliable decision-making in traffic control and management. Recent advances in low-rank tensor recovery algorithms have shown strong potential in capturing the inherent structure of high-dimensional traffic data and restoring degraded observations. However, traditional batch-based methods demand substantial computational and storage resources, which limits their scalability in the face of continuously expanding traffic data volumes. Moreover, recent online tensor recovery methods often suffer from severe performance degradation in complex real-world scenarios due to their insufficient exploitation of the intrinsic structural properties of traffic data. To address these challenges, we reformulate the traffic data recovery problem within a streaming framework, and propose a novel online robust tensor recovery algorithm that simultaneously leverages both the global spatio-temporal correlations and local consistency of traffic data, achieving high recovery accuracy and significantly improved computational efficiency in large-scale scenarios. Our method is capable of simultaneously handling missing and anomalous values in traffic data, and demonstrates strong adaptability across diverse missing patterns. Experimental results on three real-world traffic datasets demonstrate that the proposed approach achieves high recovery accuracy while significantly improving computational efficiency by up to three orders of magnitude compared to state-of-the-art batch-based methods. These findings highlight the potential of the proposed approach as a scalable and effective solution for traffic data quality enhancement in ITS.

Yao Wang, Yiyang Yang, Kaidong Wang et al.

We consider the problem of matrix completion with graphs as side information depicting the interrelations between variables. The key challenge lies in leveraging the similarity structure of the graph to enhance matrix recovery. Existing approaches, primarily based on graph Laplacian regularization, suffer from several limitations: (1) they focus only on the similarity between neighboring variables, while overlooking long-range correlations; (2) they are highly sensitive to false edges in the graphs and (3) they lack theoretical guarantees regarding statistical and computational complexities. To address these issues, we propose in this paper a novel graph regularized matrix completion algorithm called GSGD, based on preconditioned projected gradient descent approach. We demonstrate that GSGD effectively captures the higher-order correlation information behind the graphs, and achieves superior robustness and stability against the false edges. Theoretically, we prove that GSGD achieves linear convergence to the global optimum with near-optimal sample complexity, providing the first theoretical guarantees for both recovery accuracy and efficacy in the perspective of nonconvex optimization. Our numerical experiments on both synthetic and real-world data further validate that GSGD achieves superior recovery accuracy and scalability compared with several popular alternatives.

Qianxin Yi, Chenhao Wang, Kaidong Wang et al.

Low-tubal-rank tensor approximation has been proposed to analyze large-scale and multi-dimensional data. However, finding such an accurate approximation is challenging in the streaming setting, due to the limited computational resources. To alleviate this issue, this paper extends a popular matrix sketching technique, namely Frequent Directions, for constructing an efficient and accurate low-tubal-rank tensor approximation from streaming data based on the tensor Singular Value Decomposition (t-SVD). Specifically, the new algorithm allows the tensor data to be observed slice by slice, but only needs to maintain and incrementally update a much smaller sketch which could capture the principal information of the original tensor. The rigorous theoretical analysis shows that the approximation error of the new algorithm can be arbitrarily small when the sketch size grows linearly. Extensive experimental results on both synthetic and real multi-dimensional data further reveal the superiority of the proposed algorithm compared with other sketching algorithms for getting low-tubal-rank approximation, in terms of both efficiency and accuracy.

Shao-Bo Lin, Kaidong Wang, Yao Wang et al.

Compared with avid research activities of deep convolutional neural networks (DCNNs) in practice, the study of theoretical behaviors of DCNNs lags heavily behind. In particular, the universal consistency of DCNNs remains open. In this paper, we prove that implementing empirical risk minimization on DCNNs with expansive convolution (with zero-padding) is strongly universally consistent. Motivated by the universal consistency, we conduct a series of experiments to show that without any fully connected layers, DCNNs with expansive convolution perform not worse than the widely used deep neural networks with hybrid structure containing contracting (without zero-padding) convolution layers and several fully connected layers.