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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Shen, Yang Zhang, Jian Li, Yu Collaborative Innovation Center of Assessment for Basic Education Quality Beijing Normal University Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beijing Normal University Faculty of Psychology Beijing China

ISBN: (纸本)9798350394610

With the development of the concept of education evaluation, it is a key issue to effectively and accurately detect and analyze the behavior categories of teachers and students in the classroom. Firstly, this paper investigates the common behavior categories of students and teachers in the classroom. And on this basis, we sorts out 7 major behavior categories that students are usually involved in the classroom. Then, computer vision and artificial intelligence techniques are introduced to design an intelligent classroom behavior recognition system for teachers and students. Finally, the system is applied to various types of datasets for testing to verify the effectiveness of the system. Based on computer vision and artificial intelligence technology, the detection and analysis of students learning behavior in classroom can provide timely data support for teachers to grasp students' classroom learning status, optimize teaching design and implementation. © 2023 IEEE.

关键词： Students

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Cumulus cloud modeling from images based on VAE-GAN

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virtual reality & Intelligent Hardware 2021年第2期3卷 171-181页

作者： Zili ZHANG Yunchi CEN Fan ZHANG Xiaohui LIANG State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Shijiazhuang UniversityShijiazhuang 050035China

Background Cumulus clouds are important elements in creating virtual outdoor *** cumulus clouds that have a specific shape is difficult owing to the fluid nature of the ***-based modeling is an efficient method to solve this *** of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development *** In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single *** method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial ***,a 3D cloud shape is mapped into a unique hidden space using the proposed ***,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered *** train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus *** cumulus clouds were rendered under different lighting *** The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing ***,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction *** The proposed autoencoder network learns the latent space of 3D cumulus cloud *** presented reconstruction architecture models a cloud from a single *** demonstrated the effectiveness of the two models.

关键词： 3D cloud model 3D autoencoder network Generative adversarial network

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Diffusing Coupling High-Frequency-Purifying Structure Feature Extraction for Brain Multimodal Registration

Diffusing Coupling High-Frequency-Purifying Structure Featur...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Gao, Fan He, Yuanbo Li, Shuai Hao, Aimin Cao, Desen Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beihang University Beijing Advanced Innovation Center for Biomedical Engineering Beijing China Chinese PLA General Hospital Department of Biomedical Engineering Beijing China

ISBN: (纸本)9798350337488

The core of medical image registration is the alignment of corresponding structures. However, in multimodal image registration, substantial differences in appearance (intensity distribution) of the images often compel the registration model to prioritize intensity information over structure information, resulting in low accuracy of registration. Therefore, the disentangling structure information from intensity information is vital to improve the registration effectiveness. To this end, we propose a diffusing coupling high-frequency-purifying structure feature extraction for brain multimodal registration. Specifically, the denoising diffusion probabilistic models (DDPM) is firstly utilized to extract complete feature information from images. Then, the discrete cosine transform (DCT) is applied to purify high-frequency structure information from the complete feature information for registration. Furthermore, structure consistency constraint (SCC) is introduced based on purified structure information to emphasize the core position of the structure in registration. Through comprehensive comparisons with traditional and learning-based methods on the multimodal brain MRI dataset, our method demonstrates superior accuracy and stability in brain multimodal registration. Our code is available at https://***/goghfan/DDNet. © 2023 IEEE.

关键词： denoising diffusion probabilistic models high frequency purifying multimodal registration structure consistency constraint

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Super-resolution of Solar Magnetograms Using Deep Learning

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Research in Astronomy and Astrophysics 2022年第8期22卷 218-229页

作者： Fengping Dou Long Xu Zhixiang Ren Dong Zhao Xinze Zhang State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng National Laboratory Shenzhen 518000China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Currently,data-driven models of solar activity forecast are investigated extensively by using machine *** model training,it is highly demanded to establish a large database which may contain observations coming from different instruments with different spatio-temporal *** this paper,we employ deep learning models for super-resolution(SR)of magnetogram of Michelson Doppler Imager(MDI)in order to achieve the same spatial resolution of Helioseismic and Magnetic Imager(HMI).First,a generative adversarial network(GAN)is designed to transfer characteristics of MDI onto downscaled HMI,getting low-resolution HMI magnetogram in the same domain as ***,with the paired low-resolution and high-resolution HMI magnetograms,another GAN is trained in a supervised learning way,which consists of two streams,one is for generating high-fidelity image content,the other is explicitly optimized for generating elaborate image ***,these two streams work together to guarantee both high-fidelity and photorealistic super-resolved *** results demonstrate that the proposed method can generate super-resolved magnetograms with perceptual-pleasant visual ***,the best PSNR,LPIPS,RMSE,comparable SSIM and CC are obtained by the proposed *** source code and data set can be accessed via https://***/filterbank/SPSR.

关键词： techniques:image processing Sun:magnetic fields Sun:atmosphere

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Anomaly detection by exploiting the tracking trajectory in surveillance videos

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Science China(Information Sciences) 2020年第5期63卷 197-199页

作者： Zixuan XUE Wei WU Faculty of Information Technology Beijing University of Technology State Key Laboratory of Virtual Reality Technology and Systems Beihang University

Surveillance systems have become increasingly ubiquitous, which has led to a requirement to detect anomalies for efficiently preventing terrorism and reducing crimes. The increasing number of surveillance networks has imposed major technical challenges on intelligent anomaly detection because of the inconsistent appearance of pedestrians owing to posture deformation and clutter in video

关键词： Anomaly detection by exploiting the tracking trajectory in surveillance videos frame

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Unified Knowledge Maintenance Pruning and Progressive Recovery with Weight Recalling for Large Vision-Language Models 39

Unified Knowledge Maintenance Pruning and Progressive Recove...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wu, Zimeng Chen, Jiaxin Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)157735897X

Large Vision-Language Model (LVLM), leveraging Large Language Model (LLM) as the cognitive core, has recently become one of the most representative multimodal model paradigms. However, with the expansion of unimodal branches, i.e. visual encoder and LLM, the storage and computational burdens intensify, posing challenges for deployment. Structured pruning has proved promising in compressing large models by trimming a large portion of insignificant network structures. Nevertheless, most of them are predominantly designed for LLMs, either relying on unitary importance metrics that fail to deal with modality-wise imbalances or adopting generic pruning and recovery paradigms that overlook the unique calibration status and capability requirements of large models, leading to substantial performance degradation. To address these issues, we propose a novel structured pruning approach for LVLMs, dubbed Unified Knowledge Maintenance Pruning and Progressive Recovery with Weight Recalling (UKMP). Specifically, we design a Unified Knowledge Maintenance Importance (UKMI) metric, which simultaneously considers balancing the block-wise and modality-wise importance by adaptive normalization, optimizing the importance estimation by refining gradient-based criteria, and maintaining the knowledge capacity of LVLMs by using the angle distribution information entropy. Moreover, we develop a LoRA-based Progressive Distillation (LPD) method that recalls the pruned weights and performs progressive distillation for comprehensive recovery. Extensive experimental results across various vision-language tasks demonstrate the effectiveness of our approach, comparing to the state-of-the-art structured pruning methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge engineering

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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Semantic part segmentation of single-view point cloud

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Science China(Information Sciences) 2020年第12期63卷 251-253页

作者： Haotian PENG Bin ZHOU Liyuan YIN Kan GUO Qinping ZHAO State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang University

As a classic topic in computer graphics, the semantic part segmentation of 3D data is helpful for3D part-level editing and modeling. Single-views point cloud is the raw format of 3D data. Giving each point a semantic annotation in single-view point cloud, i.e., single-view point cloud semantic part segmentation, is meaningful and challenging.

关键词： computers and graphics single-view point cloud annotation transfer semantic part segmentation category independent matching point clouds registration

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What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 575-579页

作者： Fei-Yue Wang Qinghai Miao Xuan Li Xingxia Wang Yilun Lin School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China Urban Computing Lab Department of Applied ResearchShanghai AI LaboratoryShanghai 200232China IEEE

THE well-known ancient Chinese philosopher Lao Tzu(老子)or Laozi(6th~4th century BC during the Spring and Autumn period)started his classic Tao Teh Ching《道德经》or Dao De Jing(see Fig.1)with six Chinese characters:"道(Dao)可(Ke)道(Dao)非(Fei)常(Chang)道(Dao)",which has been traditionally interpreted as“道可道,非常道”or"The Dao that can be spoken is not the eternal Dao".

关键词： DAO Intelligence Spring

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Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment

arXiv

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arXiv 2024年

作者： Qi, Yunshan Zhu, Lin Zhao, Yifan Bao, Nan Li, Jia State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

Neural Radiance Fields (NeRF) achieves impressive 3D representation learning and novel view synthesis results with high-quality multi-view images as input. However, motion blur in images often occurs in low-light and high-speed motion scenes, which significantly degrades the reconstruction quality of NeRF. Previous deblurring NeRF methods struggle to estimate pose and lighting changes during the exposure time, making them unable to accurately model the motion blur. The bio-inspired event camera measuring intensity changes with high temporal resolution makes up this information deficiency. In this paper, we propose Event-driven Bundle Adjustment for Deblurring Neural Radiance Fields (EBAD-NeRF) to jointly optimize the learnable poses and NeRF parameters by leveraging the hybrid event-RGB data. An intensity-change-metric event loss and a photo-metric blur loss are introduced to strengthen the explicit modeling of camera motion blur. Experiments on both synthetic and real-captured data demonstrate that EBAD-NeRF can obtain accurate camera trajectory during the exposure time and learn a sharper 3D representations compared to prior works. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D modeling

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