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Proceedings of the 41st International Conference on Machine Learning

作者： Yibo Zhou Hai-Miao Hu Yirong Xiang Xiaokang Zhang Haotian Wu Beijing Key Laboratory of Digital Media School of Computer Science and Engineering and State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Key Laboratory of Digital Media School of Computer Science and Engineering and State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China and Hangzhou Innovation Institute of Beihang University Hangzhou China The University of Manchester UK

Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (1) label imbalance: model predictions lean greatly towards the side of majority labels; (2) semantics imbalance: model is easily overfitted on the under-represented attributes due to their insufficient semantic diversity. To render perfect label balancing, we propose a novel framework that successfully decouples label-balanced data re-sampling from the curse of attributes co-occurrence, i.e., we equalize the sampling prior of an attribute while not biasing that of the co-occurred others. To diversify the attributes semantics and mitigate the feature noise, we propose a Bayesian feature augmentation method to introduce true in-distribution novelty. Handling both imbalances jointly, our work achieves best accuracy on various popular benchmarks, and importantly, with minimal computational budget.

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Online Generation of Smooth Gliding Re-entry Trajectory

Online Generation of Smooth Gliding Re-entry Trajectory

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Hu, Cunyi Wang, Chenyu Wu, Yunjie State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Science and Technology On Aircraft Control Laboratory Beijing100191 China

ISBN: (纸本)9789811966125

Since the 21st century, the trajectory planning of hypersonic vehicles has become a major focus and difficulty of research due to their ability to strike targets rapidly. In this paper, a mathematical model of the re-entry corridor under the altitude-velocity profile is established, and the initial altitude range is obtained by using the initial descent method. The proposed equilibrium gliding condition is used to suppress the periodic altitude bounce within the remaining range while introducing the angle feedback. It is also studied in comparison with two cases. In the first case, a comparative study is performed with the method without feedback, and the second case is the application of the algorithm introducing altitude feedback in the predictor-corrector guidance algorithm to this trajectory planning. The experimental results show that this method can make the trajectory descend according to the altitude-velocity trajectory curve obtained from the preset equilibrium gliding angle, so as to obtain the three-degree-of-freedom re-entry trajectory and complete the online generation and tracking of the three-degree-of-freedom trajectory. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing 42

Enabling Communication-Efficient Federated Learning via Dist...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Guan, Yixuan Liu, Xuefeng Ren, Tao Niu, Jianwei Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Laboratory for Internet Software Technologies Institute of Software Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach. © 2023 IEEE.

关键词： Compressed sensing

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SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition 42

SVDFed: Enabling Communication-Efficient Federated Learning ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Haolin Liu, Xuefeng Niu, Jianwei Tang, Shaojie Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou China Naveen Jindal School Management University of Texas at Dallas Richardson United States

ISBN: (纸本)9798350334142

Federated learning (FL) is an emerging paradigm of distributed machine learning. However, when applied to wireless network scenarios, FL usually suffers from high communication cost because clients need to transmit their updated gradients to a server in every training round. Although many gradient compression techniques like sparsification and quantization are proposed, they compress clients' gradients independently, without considering the correlations among gradients. In this paper, we propose SVDFed, a collaborative gradient compression framework for FL. SVDFed utilizes Singular Value Decomposition (SVD) to find a few basis vectors, whose linear combination can well represent clients' gradients at a certain round. Due to the correlations among gradients, these basis vectors can still well approximate new gradients in many subsequent rounds. With the help of basis vectors, clients only need to upload the coefficients of the linear combination to the server, which greatly reduces communication cost. In addition, SVDFed leverages the classical PID (Proportional, Integral, Derivative) control to determine the proper time to update basis vectors to maintain their representation ability. Through experiments, we demonstrate that SVDFed outperforms existing gradient compression methods in FL. For example, compared to a popular gradient quantization method QSGD, SVDFed can reduce the communication overhead by 66 % and pending time by 99 %. © 2023 IEEE.

关键词： Singular value decomposition

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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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Relational Clustering-Based Parallel Spaces Construction and Embedding for Dynamic Knowledge Graph

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IEEE Transactions on Big Data 2025年

作者： Liu, Yao Zhang, Yongfei Beihang University Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beijing100191 China Beihang University School of Computer Science and Engineering China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Pengcheng Laboratory Shenzhen518055 China

With the increasing amount of data in various domains, knowledge graphs (KGs) have become powerful tools for representing complex and heterogeneous information in a structured way, and for extracting valuable information from knowledge graphs through embedding techniques to support downstream tasks such as recommendation and Q&A systems. Knowledge graphs consist of triples that are continuously added as knowledge is updated. However, most existing embedding models are designed for static graphs, requiring the entire model to be retrained for each update, which is time-consuming. Existing global dynamic embedding models focus on exploiting the structural and relational information of the whole graph to achieve embedding quality, resulting in reduced dynamic efficiency. To address this problem, we propose a relational clustering-based parallel space model in which knowledge from different domains is embedded in different subspaces, allowing each subspace to focus on the data characteristics of a specific domain, thereby improving the quality of knowledge. Second, the new data only affects some subspaces but not the performance of other spaces, improving the model's adaptability to dynamics. Furthermore, we employ two incremental approaches based on the type of added data to improve the efficiency of dynamic embedding while ensuring that the added data preserves the characteristics of the parallel space. The experimental results show that the dynamic embedding efficiency of our model is improved by an average of 50.3% compared to the SOTA dynamic embedding model for the link prediction task. Particularly on FB15K, our model not only improves the efficiency by 41% but also increases the accuracy by 7.5%, demonstrating the accuracy and efficiency of our model. © 2015 IEEE.

关键词： Knowledge graph

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"achi Mukua Experience Space"-A Multi-Player Immersive Experience of Yunnan ICH Dance 23

"achi Mukua Experience Space"-A Multi-Player Immersive Exper...

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2023 SIGGRAPH Asia 2023 Art Papers, SA 2023

作者： Luo, Siyu Yan, Shuo School of New Media Arts and Design Beihang University Beijing China School of New Media Arts and Design Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9798400703201

Increasingly, intangible cultural heritage (ICH) dance is being performed interactively through immersive experiences. However, these experiences often leaving participants isolated and lacking the connection to the cultural context of ICH dances. This project focuses on designing interactive mechanism that combine collaborative multi-players interactions with the collective nature of ICH dance. It explores the advantage of bio-signals as visual feedback in multi-player dance interactions. The project aims to create an immersive social space where participants can be fully immersed in the dance atmosphere and gain a deeper understanding of intangible heritage. © 2023 ACM.

关键词： Visual communication

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Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition

arXiv

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arXiv 2023年

作者： Chen, Cheng Zhao, Yifan Li, Jia State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China School of Computer Science Peking University China

Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets. Existing literature mainly focuses on label completion and co-occurrence learning while facing difficulties with the most common single-positive label manner. To tackle this problem, we present a semantic contrastive bootstrapping (Scob) approach to gradually recover the cross-object relationships by introducing class activation as semantic guidance. With this learning guidance, we then propose a recurrent semantic masked transformer to extract iconic object-level representations and delve into the contrastive learning problems on multi-label classification tasks. We further propose a bootstrapping framework in an Expectation-Maximization fashion that iteratively optimizes the network parameters and refines semantic guidance to alleviate possible disturbance caused by wrong semantic guidance. Extensive experimental results demonstrate that the proposed joint learning framework surpasses the state-of-the-art models by a large margin on four public multi-label image recognition benchmarks. Codes can be found at https://***/iCVTEAM/Scob. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Incentivizing Fresh Information Acquisition via Age-Based Reward

IEEE Transactions on Networking

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IEEE Transactions on Networking 2025年第3期33卷 1174-1188页

作者： Zhiyuan Wang Qingkai Meng Shan Zhang Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beijing China Institute of Artificial Intelligence Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing China

Many Internet platforms are information-oriented and crowd-based. They collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer rewards to incentivize users and compensate their costs incurred from information acquisition. In practice, a user’s cost is his/her private information, thus both the user cost and its distribution are hidden to the platform, making it challenging to determine the optimal rewarding decision. In this paper, we investigate how the platform dynamically rewards the users, aiming to jointly reduce the age of information (AoI) and the operational expenditure (OpEx). Due to the hidden cost distribution, this is an online non-convex learning problem with bandit feedback. To overcome the challenge, we first design an age-based reward scheme, which decouples the OpEx from the unknown cost distribution and enables the platform to accurately control its OpEx. We then take advantage of the age-based reward scheme and propose an exponentially discretizing and learning (EDAL) policy for platform operation. We prove that the EDAL policy performs asymptotically as well as the optimal decision (derived from the cost distribution). Simulation results show that the age-based reward scheme protects the platform’s OpEx from the influence of the user crowd characteristics, and also verify the asymptotic optimality of the EDAL policy.

关键词： Costs Reviews Soft sensors Pricing Optimization Electronic mail Stochastic processes Simulation Laboratories Internet

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PGNeXt: High-Resolution Salient Object Detection via Pyramid Grafting Network

arXiv

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arXiv 2024年

作者： Xia, Changqun Xie, Chenxi He, Zhentao Yu, Tianshu Li, Jia The Peng Cheng Laboratory Shenzhen518055 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

We present an advanced study on more challenging high-resolution salient object detection (HRSOD) from both dataset and network framework perspectives. To compensate for the lack of HRSOD dataset, we thoughtfully collect a large-scale high resolution salient object detection dataset, called UHRSD, containing 5,920 images from real-world complex scenarios at 4K-8K resolutions. All the images are finely annotated in pixel-level, far exceeding previous low-resolution SOD datasets. Aiming at overcoming the contradiction between the sampling depth and the receptive field size in the past methods, we propose a novel one-stage framework for HR-SOD task using pyramid grafting mechanism. In general, transformer-based and CNN-based backbones are adopted to extract features from different resolution images independently and then these features are grafted from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different branches. Comprehensive experiments on UHRSD and widely-used SOD datasets demonstrate that our method can simultaneously locate salient object and preserve rich details, outperforming state-of-the-art methods. To verify the generalization ability of the proposed framework, we apply it to the camouflaged object detection (COD) task. Notably, our method performs superior to most state-of-the-art COD methods without bells and whistles. © 2024, CC BY.

关键词： Grafting (chemical)

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