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学者姓名：孔德慧
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Abstract ：
3D pose estimation remains a challenging task since human poses exhibit high ambiguity and multigranularity. Traditional graph convolution networks (GCNs) accomplish the task by modeling all skeletons as an entire graph, and are unable to fuse combinable part-based features. By observing that human movements occur due to part of human body (i.e. related skeletons and body components, known as the poselet) and those poselets contribute to each movement in a hierarchical fashion, we propose a hierarchical poselet-guided graph convolutional network (HPGCN) for 3D pose estimation from 2D poses. HPGCN sets five primitives of human body as basic poselets, and constitutes high-level poselets according to the kinematic configuration of human body. Moreover, HPGCN forms a fundamental unit by using a diagonally dominant graph convolution layer and a non-local layer, which corporately capture the multi-granular feature of human poses from local to global perspective. Finally HPGCN designs a geometric constraint loss function with constraints on lengths and directions of bone vectors, which help produce reasonable pose regression. We verify the effectiveness of HPGCN on three public 3D human pose benchmarks. Experimental results show that HPGCN outperforms several state-of-the-art methods. (c) 2021 Elsevier B.V.
Keyword ：
Graph convolutional network Graph convolutional network Hierarchical poselet Hierarchical poselet Geometric constraint Geometric constraint Diagonally dominant graph convolution Diagonally dominant graph convolution Pose estimation Pose estimation
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| GB/T 7714 | Wu, Yongpeng , Kong, Dehui , Wang, Shaofan et al. HPGCN: Hierarchical poselet-guided graph convolutional network for 3D pose estimation [J]. | NEUROCOMPUTING , 2022 , 487 : 243-256 . |
| MLA | Wu, Yongpeng et al. "HPGCN: Hierarchical poselet-guided graph convolutional network for 3D pose estimation" . | NEUROCOMPUTING 487 (2022) : 243-256 . |
| APA | Wu, Yongpeng , Kong, Dehui , Wang, Shaofan , Li, Jinghua , Yin, Baocai . HPGCN: Hierarchical poselet-guided graph convolutional network for 3D pose estimation . | NEUROCOMPUTING , 2022 , 487 , 243-256 . |
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Abstract ：
Featured Application The vehicle detection algorithm proposed in this work could be used in autonomous driving systems to understand the environment, or could be applied in surveillance systems to extract useful transportation information through a camera. Vehicle detection as a special case of object detection has practical meaning but faces challenges, such as the difficulty of detecting vehicles of various orientations, the serious influence from occlusion, the clutter of background, etc. In addition, existing effective approaches, like deep-learning-based ones, demand a large amount of training time and data, which causes trouble for their application. In this work, we propose a dictionary-learning-based vehicle detection approach which explicitly addresses these problems. Specifically, an ensemble of sparse-and-dense dictionaries (ESDD) are learned through supervised low-rank decomposition; each pair of sparse-and-dense dictionaries (SDD) in the ensemble is trained to represent either a subcategory of vehicle (corresponding to certain orientation range or occlusion level) or a subcategory of background (corresponding to a cluster of background patterns) and only gives good reconstructions to samples of the corresponding subcategory, making the ESDD capable of classifying vehicles from background even though they exhibit various appearances. We further organize ESDD into a two-level cascade (CESDD) to perform coarse-to-fine two-stage classification for better performance and computation reduction. The CESDD is then coupled with a downstream AdaBoost process to generate robust classifications. The proposed CESDD model is used as a window classifier in a sliding-window scan process over image pyramids to produce multi-scale detections, and an adapted mean-shift-like non-maximum suppression process is adopted to remove duplicate detections. Our CESDD vehicle detection approach is evaluated on KITTI dataset and compared with other strong counterparts; the experimental results exhibit the effectiveness of CESDD-based classification and detection, and the training of CESDD only demands small amount of time and data.
Keyword ：
ensemble learning ensemble learning object detection object detection dictionary learning dictionary learning vehicle detection vehicle detection
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| GB/T 7714 | Rong, Zihao , Wang, Shaofan , Kong, Dehui et al. A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection [J]. | APPLIED SCIENCES-BASEL , 2021 , 11 (4) . |
| MLA | Rong, Zihao et al. "A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection" . | APPLIED SCIENCES-BASEL 11 . 4 (2021) . |
| APA | Rong, Zihao , Wang, Shaofan , Kong, Dehui , Yin, Baocai . A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection . | APPLIED SCIENCES-BASEL , 2021 , 11 (4) . |
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Abstract ：
基于时空上下文模型的RGB-D序列目标跟踪方法
Keyword ：
目标跟踪 目标跟踪 时空上下文 时空上下文 计算机视觉 计算机视觉 RGB-D RGB-D 目标动量 目标动量 机器学习 机器学习
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| GB/T 7714 | 孔德慧 , 荣子豪 , 贾思宇 et al. 基于时空上下文模型的RGB-D序列目标跟踪方法 [J]. | 孔德慧 , 2021 , 47 (3) : 224-230 . |
| MLA | 孔德慧 et al. "基于时空上下文模型的RGB-D序列目标跟踪方法" . | 孔德慧 47 . 3 (2021) : 224-230 . |
| APA | 孔德慧 , 荣子豪 , 贾思宇 , 王少帆 , 尹宝才 , 北京工业大学学报 . 基于时空上下文模型的RGB-D序列目标跟踪方法 . | 孔德慧 , 2021 , 47 (3) , 224-230 . |
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Abstract ：
三维形状重建是计算机视觉、计算机图形学、模式识别和虚拟现实等领域的重要研究课题。现有三维重建方法通常存在两个瓶颈:(1)它们涉及多个人工设计阶段,导致累积误差,且难以自动学习三维形状的语义特征;(2)它们严重依赖图像内容和质量,以及精确校准的摄像机。因此,这些方法的重建精度难以提高。基于深度学习的三维重建方法通过利用深度网络自动学习低质量图像中的三维形状语义特征,克服了这两个瓶颈。然而,这些方法具有多种体系框架,但是至今未有文献对它们作深入分析和比较。本文对基于深度学习的三维重建方法进行全面综述。首先,基于不同深度学习模型框架,将基于深度学习的三维重建方法分为4类:递归神经网络、深自编码器、生...
Keyword ：
生成对抗网络 生成对抗网络 循环神经网络 循环神经网络 三维重建 三维重建 深度学习模型 深度学习模型 深度自编码器 深度自编码器 卷积神经网络 卷积神经网络
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| GB/T 7714 | 刘彩霞 , 孔德慧 , 王少帆 et al. 深度三维重建:方法、数据和挑战（英文） [J]. | Frontiers of Information Technology & Electronic Engineering , 2021 , 22 (05) : 652-673 . |
| MLA | 刘彩霞 et al. "深度三维重建:方法、数据和挑战（英文）" . | Frontiers of Information Technology & Electronic Engineering 22 . 05 (2021) : 652-673 . |
| APA | 刘彩霞 , 孔德慧 , 王少帆 , 王志勇 , 李敬华 , 尹宝才 . 深度三维重建:方法、数据和挑战（英文） . | Frontiers of Information Technology & Electronic Engineering , 2021 , 22 (05) , 652-673 . |
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Abstract ：
Although deep networks based methods outperform traditional 3D reconstruction methods which require multiocular images or class labels to recover the full 3D geometry, they may produce incomplete recovery and unfaithful reconstruction when facing occluded parts of 3D objects. To address these issues, we propose Depth-preserving Latent Generative Adversarial Network (DLGAN) which consists of 3D Encoder-Decoder based GAN (EDGAN, serving as a generator and a discriminator) and Extreme Learning Machine (ELM, serving as a classifier) for 3D reconstruction from a monocular depth image of an object. Firstly, EDGAN decodes a latent vector from the 2.5D voxel grid representation of an input image, and generates the initial 3D occupancy grid under common GAN losses, a latent vector loss and a depth loss. For the latent vector loss, we design 3D deep AutoEncoder (AE) to learn a target latent vector from ground truth 3D voxel grid and utilize the vector to penalize the latent vector encoded from the input 2.5D data. For the depth loss, we utilize the input 2.5D data to penalize the initial 3D voxel grid from 2.5D views. Afterwards, ELM transforms float values of the initial 3D voxel grid to binary values under a binary reconstruction loss. Experimental results show that DLGAN not only outperforms several state-of-the-art methods by a large margin on both a synthetic dataset and a real-world dataset, but also predicts more occluded parts of 3D objects accurately without class labels.
Keyword ：
depth loss depth loss monocular depth image monocular depth image Three-dimensional displays Three-dimensional displays 3D reconstruction 3D reconstruction Transforms Transforms ELM ELM Image reconstruction Image reconstruction Shape Shape Generative adversarial networks Generative adversarial networks latent vector latent vector Two dimensional displays Two dimensional displays Gallium nitride Gallium nitride
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| GB/T 7714 | Liu, Caixia , Kong, Dehui , Wang, Shaofan et al. DLGAN: Depth-Preserving Latent Generative Adversarial Network for 3D Reconstruction [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 : 2843-2856 . |
| MLA | Liu, Caixia et al. "DLGAN: Depth-Preserving Latent Generative Adversarial Network for 3D Reconstruction" . | IEEE TRANSACTIONS ON MULTIMEDIA 23 (2021) : 2843-2856 . |
| APA | Liu, Caixia , Kong, Dehui , Wang, Shaofan , Li, Jinghua , Yin, Baocai . DLGAN: Depth-Preserving Latent Generative Adversarial Network for 3D Reconstruction . | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 , 2843-2856 . |
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Abstract ：
3-D action recognition is referred to as the classification of action sequences which consist of 3-D skeleton joints. While many research works are devoted to 3-D action recognition, it mainly suffers from three problems: 1) highly complicated articulation; 2) a great amount of noise; and 3) low implementation efficiency. To tackle all these problems, we propose a real-time 3-D action-recognition framework by integrating the locally aggregated kinematic-guided skeletonlet (LAKS) with a supervised hashing-by-analysis (SHA) model. We first define the skeletonlet as a few combinations of joint offsets grouped in terms of the kinematic principle and then represent an action sequence using LAKS, which consists of a denoising phase and a locally aggregating phase. The denoising phase detects the noisy action data and adjusts it by replacing all the features within it with the features of the corresponding previous frame, while the locally aggregating phase sums the difference between an offset feature of the skeletonlet and its cluster center together over all the offset features of the sequence. Finally, the SHA model combines sparse representation with a hashing model, aiming at promoting the recognition accuracy while maintaining high efficiency. Experimental results on MSRAction3D, UTKinectAction3D, and Florence3DAction datasets demonstrate that the proposed method outperforms state-of-the-art methods in both recognition accuracy and implementation efficiency.
Keyword ：
Joints Joints skeletonlet skeletonlet sparse representation sparse representation skeleton joints skeleton joints Solid modeling Solid modeling Feature extraction Feature extraction Computational modeling Computational modeling Kinematics Kinematics Action recognition Action recognition Real-time systems Real-time systems hashing hashing Analytical models Analytical models
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| GB/T 7714 | Sun, Bin , Wang, Shaofan , Kong, Dehui et al. Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2021 , 52 (6) : 4837-4849 . |
| MLA | Sun, Bin et al. "Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model" . | IEEE TRANSACTIONS ON CYBERNETICS 52 . 6 (2021) : 4837-4849 . |
| APA | Sun, Bin , Wang, Shaofan , Kong, Dehui , Wang, Lichun , Yin, Baocai . Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model . | IEEE TRANSACTIONS ON CYBERNETICS , 2021 , 52 (6) , 4837-4849 . |
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Abstract ：
Multi-view human action recognition remains a challenging problem due to large view changes. In this article, we propose a transfer learning-based framework called transferable dictionary learning and view adaptation (TDVA) model for multi-view human action recognition. In the transferable dictionary learning phase, TDVA learns a set of view-specific transferable dictionaries enabling the same actions from different views to share the same sparse representations, which can transfer features of actions from different views to an intermediate domain. In the view adaptation phase, TDVA comprehensively analyzes global, local, and individual characteristics of samples, and jointly learns balanced distribution adaptation, locality preservation, and discrimination preservation, aiming at transferring sparse features of actions of different views from the intermediate domain to a common domain. In other words, TDVA progressively bridges the distribution gap among actions from various views by these two phases. Experimental results on IXMAS, ACT4(2), and NUCLA action datasets demonstrate that TDVA outperforms state-of-the-art methods.
Keyword ：
sparse representation sparse representation Action recognition Action recognition transfer learning transfer learning multi-view multi-view
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| GB/T 7714 | Sun, Bin , Kong, Dehui , Wang, Shaofan et al. Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (2) . |
| MLA | Sun, Bin et al. "Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 15 . 2 (2021) . |
| APA | Sun, Bin , Kong, Dehui , Wang, Shaofan , Wang, Lichun , Yin, Baocai . Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (2) . |
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Abstract ：
Recovering the geometry of an object from a single depth image is an interesting yet challenging problem. While the recently proposed learning based approaches have demonstrated promising performance, they tend to produce unfaithful and incomplete 3D shape. In this paper, we propose Latent Feature-Aware and Local Structure-Preserving Network (LALP-Net) for completing the full 3D shape from a single depth view of an object, which consists of a generator and a discriminator. In the generator, we introduce Latent Feature-Aware (LFA) to learn a latent representation from the encoded input for a decoder generating the accurate and complete 3D shape. LFA can be taken as a plug-and-play component to upgrade existing networks. In the discriminator, we combine a Local Structure Preservation (LSP) module regarding visible regions and a Global Structure Prediction (GSP) module regarding entire regions for faithful reconstruction. Experimental results on both synthetic and real-world datasets show that our LALP-Net outperforms the state-of-the-art methods by a large margin.
Keyword ：
Latent shape representation Latent shape representation Depth view Depth view Local structure Local structure 3D completion 3D completion
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| GB/T 7714 | Liu, Caixia , Kong, Dehui , Wang, Shaofan et al. Latent Feature-Aware and Local Structure-Preserving Network for 3D Completion from a Single Depth View [C] . 2021 : 67-79 . |
| MLA | Liu, Caixia et al. "Latent Feature-Aware and Local Structure-Preserving Network for 3D Completion from a Single Depth View" . (2021) : 67-79 . |
| APA | Liu, Caixia , Kong, Dehui , Wang, Shaofan , Li, Jinghua , Yin, Baocai . Latent Feature-Aware and Local Structure-Preserving Network for 3D Completion from a Single Depth View . (2021) : 67-79 . |
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Abstract ：
针对理工科课程在教学内容、方式上实施"润物细无声"式课程思政的难度,以计算机图形学为例,提出课程思政建设总体思路,在阐述具体课程思政建设过程及结果的基础上,凝练总体建设原则,给出一般化的理工科课程思政建设策略,为在理工科院校更广泛、更有效地开展课程思政建设提供思路。
Keyword ：
计算机图形学 计算机图形学 课程思政 课程思政 科学方法论 科学方法论 理工科课程思政建设 理工科课程思政建设 内涵与外延 内涵与外延
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| GB/T 7714 | 孔德慧 , 李敬华 , 王立春 et al. 基于计算机图形学教学实践的理工科课程思政建设研究 [J]. | 计算机教育 , 2021 , PageCount-页数: 4 (09) : 15-18 . |
| MLA | 孔德慧 et al. "基于计算机图形学教学实践的理工科课程思政建设研究" . | 计算机教育 PageCount-页数: 4 . 09 (2021) : 15-18 . |
| APA | 孔德慧 , 李敬华 , 王立春 , 张勇 , 孙艳丰 . 基于计算机图形学教学实践的理工科课程思政建设研究 . | 计算机教育 , 2021 , PageCount-页数: 4 (09) , 15-18 . |
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Abstract ：
Three-dimensional (3D) reconstruction of shapes is an important research topic in the fields of computer vision, computer graphics, pattern recognition, and virtual reality. Existing 3D reconstruction methods usually suffer from two bottlenecks: (1) they involve multiple manually designed states which can lead to cumulative errors, but can hardly learn semantic features of 3D shapes automatically; (2) they depend heavily on the content and quality of images, as well as precisely calibrated cameras. As a result, it is difficult to improve the reconstruction accuracy of those methods. 3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep networks. However, while these methods have various architectures, in-depth analysis and comparisons of them are unavailable so far. We present a comprehensive survey of 3D reconstruction methods based on deep learning. First, based on different deep learning model architectures, we divide 3D reconstruction methods based on deep learning into four types, recurrent neural network, deep autoencoder, generative adversarial network, and convolutional neural network based methods, and analyze the corresponding methodologies carefully. Second, we investigate four representative databases that are commonly used by the above methods in detail. Third, we give a comprehensive comparison of 3D reconstruction methods based on deep learning, which consists of the results of different methods with respect to the same database, the results of each method with respect to different databases, and the robustness of each method with respect to the number of views. Finally, we discuss future development of 3D reconstruction methods based on deep learning.
Keyword ：
Deep learning models Deep learning models Deep autoencoder Deep autoencoder Convolutional neural network Convolutional neural network Recurrent neural network Recurrent neural network TP391 TP391 Generative adversarial network Generative adversarial network Three-dimensional reconstruction Three-dimensional reconstruction
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| GB/T 7714 | Liu, Caixia , Kong, Dehui , Wang, Shaofan et al. Deep3D reconstruction: methods, data, and challenges [J]. | FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING , 2021 , 22 (5) : 652-672 . |
| MLA | Liu, Caixia et al. "Deep3D reconstruction: methods, data, and challenges" . | FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING 22 . 5 (2021) : 652-672 . |
| APA | Liu, Caixia , Kong, Dehui , Wang, Shaofan , Wang, Zhiyong , Li, Jinghua , Yin, Baocai . Deep3D reconstruction: methods, data, and challenges . | FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING , 2021 , 22 (5) , 652-672 . |
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