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Unsupervised Martian Dust Storm Removal via Disentangled Representation Learning

Research in Astronomy and Astrophysics 2022年第9期22卷 255-266页

作者： Dong Zhao Jia Li Hongyu Li Long Xu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBejing 100191China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China

Mars exploration has become a hot spot in recent years and is still advancing rapidly. However, Mars has massive dust storms that may cover many areas of the planet and last for weeks or even months. The local/global dust storms are so influential that they can significantly reduce visibility, and thereby the images captured by the cameras on the Mars rover are degraded severely. This work presents an unsupervised Martian dust storm removal network via disentangled representation learning(DRL). The core idea of the DRL framework is to use the content encoder and dust storm encoder to disentangle the degraded images into content features(on domain-invariant space) and dust storm features(on domain-specific space). The dust storm features carry the full dust storm-relevant prior knowledge from the dust storm images. The “cleaned” content features can be effectively decoded to generate more natural, faithful, clear *** primary advantages of this framework are twofold. First, it is among the first to perform unsupervised training in Martian dust storm removal with a single image, avoiding the synthetic data requirements. Second, the model can implicitly learn the dust storm-relevant prior knowledge from the real-world dust storm data sets, avoiding the design of the complicated handcrafted priors. Extensive experiments demonstrate the DRL framework’s effectiveness and show the promising performance of our network for Martian dust storm removal.

关键词： planets and satellites:atmospheres planets and satellites:general planets and satellites:surfaces

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LipText: Lip Tracking Based Text Entry in VR 1st

LipText: Lip Tracking Based Text Entry in VR

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1st International Conference on Extended Reality, ICXR 2024

作者： Leng, Jiaye Wang, Zijun Wu, Jian Wang, Lili School of Creative Media City University of Hong Kong Kowloon Tong Hong Kong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789819636785

Text entry is an important task in virtual reality (VR), and most existing methods require hand involvement, while hands-free typing has great potential for applications in mobile scenarios. Existing hands-free text entry methods are usually implemented by combining the head and eyes with techniques such as Dwell, Blink and Gesture, which can easily fatigue the user. In this paper, we propose LipText, a lip-tracking-based text entry method in VR. We use a neural network to perform letter-level prediction on the lip data captured by the facial tracker and use head-based selection as an auxiliary to improve the accuracy. We conduct a user study to evaluate our method, the results show a typing speed of 8.63 WPM for the novice group, 9.81 WPM for the potential expert group, and the highest recorded typing speed is 11.13 WPM achieved by a potential expert. Our method is also novice-friendly, and their typing speed increased by 64.38% over a six-day practice. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： virtual environments

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Radiographic Reports Generation via Retrieval Enhanced Cross-modal Fusion

Radiographic Reports Generation via Retrieval Enhanced Cross...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hou, Xia Luo, Yifan Song, Wenfeng Guo, Yuting You, Wenzhe Li, Shuai Beijing Information Science and Technology University School of Computer Science Beijing China Beihang University The State Key Laboratory of Virtual Reality Technology and Systems Beijing China Stomatology Center of China-Japan Friendship Hospital Beijing China

ISBN: (纸本)9798350386226

Accurate radiographic reports are crucial for effective clinical decision-making and patient safety, as they directly influence diagnosis and treatment plans. Existing models for radiographic report generation often struggle with integrating medical image and textual report features and addressing data imbalances. To overcome these limitations, we propose an enhanced cross-modal aligned retrieval-driven network (EARnet). Our model incorporates two key innovations: Enhanced Cross-modal Alignment (ECA) module and Case-based Retrieval Augmenter (CRA) module. ECA ensures the effective integration of medical visual and textual data by aligning these modalities. CRA helps mitigate data imbalance by improving the representation of medical information and ensuring a more balanced and comprehensive coverage of both normal and abnormal cases. This dual-module approach significantly improves the coherence and accuracy of the generated medical reports. Evaluation results demonstrate that our EARnet model substantially outperforms existing methods in terms of report quality and accuracy across multiple metrics. Our codes and models are available at https://***/lyf616/EARnet. © 2024 IEEE.

关键词： Patient treatment

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Exploring the Factors of Students' Online Learning Based on Structural Equation Modeling 11

Exploring the Factors of Students' Online Learning Based on ...

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11th IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022

作者： Wan, Han Luo, Hongzhen Zhong, Zihao Yang, Jianlei Beihang University School of Computer Science and Engineering State Key Laboratory of Virtual Reality Technology and Systems Beijing China

ISBN: (纸本)9781665491174

Due to the COVID-19 pandemic, traditional teaching has been migrated online. Different from traditional face-to-face teaching, when students learn online, the online learning platform will generate various data. And these data make it possible for us to analyze students' final academic performance. In this paper, we use structural equation modeling (SEM) to analyze the relationship between students' learning factors. It is found that students' lab scores (LS), exercise scores (LS) and participation in the discussion (PID) have a direct impact on their final programming scores (FPS). This paper also finds that students' assignment submissions have an indirect influence on their final programming scores (FPS) but have a direct effect on lab scores (LS). In addition, students' participation in the discussion (PID) has an indirect influence on assignment submissions (AS) and lab scores (LS). The research in this paper can provide instructional designers with references for instructional design. © 2022 IEEE.

关键词： Students

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Generative Cooperative Network for Person Image Generation 6

Generative Cooperative Network for Person Image Generation

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6th IEEE International Conference on Universal Village, UV 2022

作者： Liu, Yang Wang, Shuai Wu, Yubin Sheng, Hao Xiong, Zhang Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China

ISBN: (纸本)9781665474771

Person image generation is to synthesize realistic pedestrian images that follow the same distribution as the given dataset. Previous attempts can be generally categorized into two classes: some methods use human pose information as guidance and others try to generate person images from scratch. The former is to transfer the pose of a source image to a reference pose. The generated person image have the same identity as the source image. The latter takes a random noise from latent space as input, and the real person images are only used as references for the discriminator. While pose-guided person image generation is widely studied, generating-from-scratch methods are also worth exploring because they can synthesize person image with new identity, which is a useful manner of data augmentation. These two types of generating methods have their different advantages and disadvantages, and sometimes they are complementary. In this work, the authors design a Generative Cooperative Network (GCN) to jointly train two types of GANs. The two GANs serve different purposes, and can learn from each other during the cooperative learning procedure. The proposed approach is verified on public datasets, and the results show that our GCN improves the performance of the baseline methods. Comparisons with state-of-the-art methods also prove the effectiveness of the proposed method. © 2022 IEEE.

关键词： computer vision

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Multi-Target Distributed Maximum Correntropy Kalman Filter 24

Multi-Target Distributed Maximum Correntropy Kalman Filter

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12th International Conference on Communications and Broadband Networking, ICCBN 2024

作者： Deng, Xingyu Han, Hongyu Zhang, Sheng Xu, Yin College of Computer Science and Visual Computing Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu610066 China The School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798400717109

Multi-target tracking in sensor networks is a challenging problem, especially in scenarios where sensor observations are limited. Conventional centralized Kalman filters and distributed Kalman filters (DKFs) require each sensor to observe all targets, which is often difficult to achieve in practical distributed applications. With this in mind, and inspired by the promising robustness of the maximum correntropy criterion in non-Gaussian noise environments, this paper proposes a novel multi-target distributed maximum correntropy Kalman filter, named MT-DMCKF. The proposed algorithm can operate effectively in distributed multi-target scenarios and provide accurate and consistent tracking performance even when the number of sensors mounted on targets is limited. Within a single period, the algorithm requires only one exchange of information among sensors, thus reducing communication overhead compared to existing distributed Kalman filters. Simulation experiments validate the effectiveness and robustness of the proposed MT-DMCKF. © 2024 ACM.

关键词： Wiener filtering

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Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition 24

Decoupling General and Personalized Knowledge in Federated L...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Xinghao Liu, Xuefeng Niu, Jianwei Wang, Haolin Tang, Shaojie Zhu, Guogang Su, Hao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Management Science and Systems University at Buffalo BuffaloNY United States School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798400706868

To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL) is to decouple general knowledge (shared among clients) and client-specific knowledge, as the latter can have a negative impact on collaboration if not removed. Existing PFL methods primarily adopt a parameter partitioning approach, where the parameters of a model are designated as one of two types: parameters shared with other clients to extract general knowledge and parameters retained locally to learn client-specific knowledge. However, as these two types of parameters are put together like a jigsaw puzzle into a single model during the training process, each parameter may simultaneously absorb both general and client-specific knowledge, thus struggling to separate the two types of knowledge effectively. In this paper, we introduce FedDecomp, a simple but effective PFL paradigm that employs parameter additive decomposition to address this issue. Instead of assigning each parameter of a model as either a shared or personalized one, FedDecomp decomposes each parameter into the sum of two parameters: a shared one and a personalized one, thus achieving a more thorough decoupling of shared and personalized knowledge compared to the parameter partitioning method. In addition, as we find that retaining local knowledge of specific clients requires much lower model capacity compared with general knowledge across all clients, we let the matrix containing personalized parameters be low rank during the training process. Moreover, a new alternating training strategy is proposed to further improve the performance. Experimental results across multiple datasets and varying degrees of data heterogeneity demonstrate that FedDecomp outperforms state-of-the-art methods up to 4.9%. The code is available at https://***/XinghaoWu/FedDecomp © 2024 ACM.

关键词： Federated learning

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IAACS: Image aesthetic assessment through color composition and space formation

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virtual Reality & Intelligent Hardware 2023年第1期5卷 42-56页

作者： Bailin YANG Changrui ZHU Frederick WBLI Tianxiang WEI Xiaohui LIANG Qingxu WANG School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China Department of Computer Science University of DurhamDH13LEUnited Kingdom College of Intelligence and Computing Tianjin UniversityTianjin 300072China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China

Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.

关键词： Image aesthetic assessment Color composition Space formation Deep learning

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Skinned Motion Retargeting with Preservation of Body Part Relationships

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 1-13页

作者： Zhang, Jia-Qi Wang, Miao Zhang, Fu-Cheng Zhang, Fang-Lue State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China School of Engineering and Computer Science Victoria University of Wellington New Zealand

Motion retargeting is an active research area in computer graphics and animation, allowing for the transfer of motion from one character to another, thereby creating diverse animated character data. While this technology has numerous applications in animation, games, and movies, current methods often produce unnatural or semantically inconsistent motion when applied to characters with different shapes or joint counts. This is primarily due to a lack of consideration for the geometric and spatial relationships between the body parts of the source and target characters. To tackle this challenge, we introduce a novel spatially-preserving Skinned Motion Retargeting Network (SMRNet) capable of handling motion retargeting for characters with varying shapes and skeletal structures while maintaining semantic consistency. By learning a hybrid representation of the character's skeleton and shape in a rest pose, SMRNet transfers the rotation and root joint position of the source character's motion to the target character through embedded rest pose feature alignment. Additionally, it incorporates a differentiable loss function to further preserve the spatial consistency of body parts between the source and target. Comprehensive quantitative and qualitative evaluations demonstrate the superiority of our approach over existing alternatives, particularly in preserving spatial relationships more effectively IEEE

关键词： Semantics

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PointHGN: Point Heterogeneous Graph Neural Network for Point Cloud Learning

PointHGN: Point Heterogeneous Graph Neural Network for Point...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Leng, Zhiying Wu, Shun-Cheng Li, Frederick W. B. Tombari, Federico Liang, Xiaohui Beihang University State Key Laboratory of Virtual Reality Technology and Systems China Technical University of Munich Computer Aided Medical Procedures & Augmented Reality Germany Durham University Department of Computer Science United Kingdom

ISBN: (纸本)9798350313154

Point cloud learning has gained significant attention in 3D multimedia. However, existing methods learn point-wise features in a homogeneous manner, disregarding the heterogeneity that inherently exists in point clouds. Embedding the heterogeneity into feature learning is favorable to learning more discriminative point-wise features with rich geometric structures. In this work, we are the first to analyze and utilize the heterogeneity of point clouds, namely geometric heterogeneity and category heterogeneity. We hence propose two general heterogeneous graph convolutions to exploit such heterogeneity, including Geometric Heterogeneous Graph Convolution (GHGC) and Category Heterogeneous Graph Convolution (CHGC). We implement a point heterogeneous graph neural network (PointHGN) based on classical MLP-based point cloud pipelines, without altering network configurations. Experimental results on point cloud semantic segmentation tasks manifest that our PointHGN achieves commendable performance on public datasets. Our proposed method provides an effective and alternative approach for point cloud learning that researchers can explore further. © 2023 IEEE.

关键词： Graph neural networks

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