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IEEE International Conference on Web Services (ICWS)

作者： Xiaowei Liu Mengmeng Sun Wenhui Chen Dianlong You Limin Shen Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the proliferation of the digital economy, cloud API has become the best carrier for data transmission and capability replication in software development. In the service-oriented software development paradigm, developers select appropriate cloud APIs for mashup creation based on their practical needs, where complementarity is an important consideration. Complementarity is reflected in various implicit relationships within mashups and cloud APIs, making complementarity analysis of cloud API ecosystems a critical issue. However, existing studies have overlooked the complementarity, and the lack of application variety in the primary dataset ProgrammableWeb has added a threat to the validity and generalization. In this paper, we first build a dataset of two cloud API ecosystems: ProgrammableWeb centering on mashup creation and Huawei AppGallery centering on mobile app development. Then, we conduct statistical and graph analysis to compare the two ecosystems. Finally, we propose a modeling approach to quantify complementarity and perform an analysis to investigate the role of complementarity in cloud API ecosystems. The results reveal differences between mashups and mobile apps in their preferences for cloud APIs. It also highlights the potential of complementarity in addressing the long-tail problem in cloud API ecosystems and improving cloud API recommendations.

关键词： Analytical models Mashups Ecosystems Mobile applications Data communication Software development management

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HOIAnimator: Generating Text-Prompt Human-Object Animations Using Novel Perceptive Diffusion Models

HOIAnimator: Generating Text-Prompt Human-Object Animations ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wenfeng Song Xinyu Zhang Shuai Li Yang Gao Aimin Hao Xia Hau Chenglizhao Chen Ning Li Hong Qin Beijing Information Science and Technology University Zhongguancun Laboratory China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Research Unit of Virtual Human and Virtual Surgery (2019RU004) Chinese Academy of Medical Sciences College of Computer Science and Technology China University of Petroleum (East China) Department of Computer Science Stony Brook University (SUNY at Stony Brook) New York USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

To date, the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research. The underlying challenge demands both a discriminating interpretation of language and a comprehen-sive physics-centric model supporting real-world dynamics. To ameliorate, this paper advocates HOIAnimator, a novel and interactive diffusion model with perception ability and also ingeniously crafted to revolutionize the animation of complex interactions from linguistic narratives. The effectiveness of our model is anchored in two ground-breaking innovations: (1) Our Perceptive Diffusion Models (PDM) brings together two types of models: one focused on hu-man movements and the other on objects. This combination allows for animations where humans and objects move in concert with each other, making the overall motion more realistic. Additionally, we propose a Perceptive Message Passing (PMP) mechanism to enhance the communication bridging the two models, ensuring that the animations are smooth and unified; (2) We devise an Interaction Contact Field (ICF), a sophisticated model that implicitly captures the essence of HOls. Beyond mere predictive contact points, the ICF assesses the proximity of human and object to their respective environment, informed by a probabilistic distribution of interactions learned throughout the denoising phase. Our comprehensive evaluation showcases HOlani-mator's superior ability to produce dynamic, context-aware animations that surpass existing benchmarks in text-driven animation synthesis.

关键词： computer vision Technological innovation Message passing Computational modeling Noise reduction Linguistics Animation

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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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Audio-visual aware Foveated Rendering

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Shi, Xuehuai Li, Yucheng Li, Jiaheng Wu, Jian Yin, Jieming Chen, Xiaobai Wang, Lili Nanjing University of Posts and Telecommunications School of Computer Science Jiangsu 210023 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University School of Computer Science and Engineering Beijing100191 China

With the increasing complexity of geometry and rendering effects in virtual reality (VR) scenes, existing foveated rendering methods for VR head-mounted displays (HMDs) struggle to meet users' demands for VR scene rendering with high frame rates (≥ 60 f ps for rendering binocular foveated images in VR scenes containing over 50M triangles). Current research validates that auditory content affects the perception of the human visual system (HVS). However, existing foveated rendering methods primarily model the HVS's eccentricity-dependent visual perception ability on the visual content in VR while ignoring the impact of auditory content on the HVS's visual perception. In this paper, we introduce an auditory-content-based perceived rendering quality analysis to quantify the impact of visual perception under different auditory conditions in foveated rendering. Based on the analysis results, we propose an audio-visual aware foveated rendering method (AvFR). AvFR first constructs an audio-visual feature-driven perception model that predicts the eccentricity-based visual perception in real time by combining the scene's audio-visual content, and then proposes a foveated rendering cost optimization algorithm to adaptively control the shading rate of different regions with the guidance of the perception model. In complex scenes with visual and auditory content containing over 1.17M triangles, AvFR renders high-quality binocular foveated images at an average frame rate of 116 f ps. The results of the main user study and performance evaluation validate that AvFR achieves significant performance improvement (up to 1.4× speedup) without lowering the perceived visual quality compared with the state-of-the-art VR-HMD foveated rendering method. © 1995-2012 IEEE.

关键词： Foveated Rendering Perception-driven Rendering virtual Reality

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EPI-Guided Cost Construction Network for Light Field Disparity Estimation

EPI-Guided Cost Construction Network for Light Field Dispari...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Tun Chen, Rongshan Cong, Ruixuan Yang, Da Cui, Zhenglong Li, Fangping Sheng, Hao Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

Recent learning-based light field (LF) disparity estimation methods construct cost volume by sequentially shifting each sub-aperture image (SAI) with a series of predefined offsets. They only use the visual information of SAIs and lose the geometry of LF. In this paper, we design a simple network that can cleverly integrate EPI features with cost volume to estimate the disparity. Firstly, we propose an efficient EPI extraction module to use abundant line characteristics. Secondly, we offer an EPI-Cost volume construction module that can create volume guided by the EPI line and the color consistency of images. Finally, after completing it, we adopt an inter-volume fusion module to considerably correlate the validity of EPI lines in both directions. Experimental results show the proposed method achieves state-of-the-art performance in the quantitative and qualitative evaluation of the UrbanLF-Syn dataset. © 2023 IEEE.

关键词： Cost estimating

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Semantic recommendation through Reinforcement Learning and weighted meta-paths

Semantic recommendation through Reinforcement Learning and w...

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2022 International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2022

作者： Yu, Jing Lang, Yue Li, Xuewen Zhang, Jin Zheng, Shaojie Gong, Jibing School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

Explainable recommendation systems, which can produce high-accuracy recommendations and help users make quick decisions, have become a hotspot in research field. Most of existing research algorithms committed to improving the accuracy of recommendation, but ignored the interpretability of recommendation. We propose a semantic recommendation algorithm through Reinforcement Learning and weighted meta-paths which analyzes and selects the meta-path, and uses Reinforcement Learning network to train the weight of meta-path. This method can effectively solve data sparsity, improve the accuracy of recommendation, and increase interpretability. Compared with the baseline method, indicators of the recommended methods for integrating Reinforcement Learning with Heterogeneous Information Networks have been greatly improved, as verified and compared on a real MovieLens 1M dataset. Experimental results prove that the algorithm can effectively improve the accuracy and interpretability of the recommendation. © 2022 SPIE.

关键词： Reinforcement learning

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Image Desaturation for SDO/AIA Using Mixed Convolution Network

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Research in Astronomy and Astrophysics 2022年第6期22卷 91-100页

作者： Xuexin Yu Long Xu Zhixiang Ren Dong Zhao Wenqing Sun National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng National Laboratory Shenzhen 518000China National Space Science Center Chinese Academy of SciencesBeijing 100190China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100190China

The Atmospheric Imaging Assembly(AIA)onboard the Solar Dynamics Observatory(SDO)provides full-disk solar images with high temporal cadence and spatial resolution over seven extreme ultraviolet(EUV)wave ***,as violent solar flares happen,images captured in EUV wave bands may have saturation in active regions,resulting in signal *** this paper,we propose a deep learning model to restore the lost signal in saturated regions by referring to both unsaturated/normal regions within a solar image and statistical probability model of massive normal solar *** proposed model,namely mixed convolution network(MCNet),is established over conditional generative adversarial network(GAN)and the combination of partial convolution(PC)and validness migratable convolution(VMC).These two convolutions were originally proposed for image *** addition,they are implemented only on unsaturated/valid pixels,followed by certain compensation to compensate the deviation of PC/VMC relative to normal *** results demonstrate that the proposed MCNet achieves favorable desaturated results for solar images and outperforms the state-of-the-art methods both quantitatively and qualitatively.

关键词： Sun:activity Sun atmosphere-Sun chromosphere

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Energy-efficient computation offloading via deep reinforcement learning in mobility-aware multi-access edge computing systems with diverse users

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Expert Systems with Applications 2025年 286卷

作者： Wu, Haixing Jin, Shunfu School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China

Multi-access Edge Computing (MEC) has been a promising solution that enables Internet of Things (IoT) devices to support computation-intensive applications by offloading some tasks to the network edge. However, most existing offloading methods in MEC systems are based on the premise of scenarios with either single-type or stable users, which is not aligned with the practical situations of users’ diversity and mobility. In addition, these methods always ignore energy saving strategies in MEC systems, which inevitably degrades Utility Energy Efficiency (UEE). To tackle these issues, considering personalized requirements from diverse users and idle energy consumption in MEC servers, we propose a novel computation offloading architecture with adaptive sleeping mechanism and heterogeneous MEC servers. Based on the architecture, we investigate energy-efficient computation offloading in mobility-aware MEC systems with diverse users. Under the Deep Reinforcement Learning (DRL) framework, we propose a Twin Delayed Deep Deterministic Policy Gradient Algorithm with Bursty Traffic (TD3-BT), in which the inherent correlation of task arrivals is taken into account to capture the behavior of correlated traffic and provide rational metrics estimation within one time slot. A long-term system cost minimization problem is formulated to optimize computation offloading for the trade-off between system delay and UEE. Assisted by TD3-BT, the formulated problem is effectively solved and the decision making in each time slot is provided for diverse users. Experimental results demonstrate that our TD3-BT algorithm achieves superior performance under various offloading scenarios compared to some benchmarks. © 2025 Elsevier Ltd

关键词： Energy efficiency

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PHA: Patch-Wise High-Frequency Augmentation for Transformer-Based Person Re-Identification

PHA: Patch-Wise High-Frequency Augmentation for Transformer-...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Guiwei Zhang Yongfei Zhang Tianyu Zhang Bo Li Shiliang Pu Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University Pengcheng Laboratory Hikvision Research Institute

Although recent studies empirically show that injecting Convolutional Neural Networks (CNNs) into Vision Transformers (ViTs) can improve the performance of person reidentification, the rationale behind it remains elusive. From a frequency perspective, we reveal that ViTs perform worse than CNNs in preserving key high-frequency components (e.g, clothes texture details) since high-frequency components are inevitably diluted by low-frequency ones due to the intrinsic Self-Attention within ViTs. To remedy such inadequacy of the ViT, we propose a Patch-wise High-frequency Augmentation (PHA) method with two core designs. First, to enhance the feature representation ability of high-frequency components, we split patches with high-frequency components by the Discrete Haar Wavelet Transform, then empower the ViT to take the split patches as auxiliary input. Second, to prevent high-frequency components from being diluted by low-frequency ones when taking the entire sequence as input during network optimization, we propose a novel patch-wise contrastive loss. From the view of gradient optimization, it acts as an implicit augmentation to improve the representation ability of key high-frequency components. This benefits the ViT to capture key high-frequency components to extract discriminative person representations. PHA is necessary during training and can be removed during inference, without bringing extra complexity. Extensive experiments on widely-used ReID datasets validate the effectiveness of our method.

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Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff

arXiv

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arXiv 2023年

作者： Li, Jia Qiao, Shengye Zhao, Zhirui Xie, Chenxi Chen, Xiaowu Xia, Changqun The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Laboratory Shenzhen518000 China

Existing salient object detection methods often adopt deeper and wider networks for better performance, resulting in heavy computational burden and slow inference speed. This inspires us to rethink saliency detection to achieve a favorable balance between efficiency and accuracy. To this end, we design a lightweight framework while maintaining satisfying competitive accuracy. Specifically, we propose a novel trilateral decoder framework by decoupling the U-shape structure into three complementary branches, which are devised to confront the dilution of semantic context, loss of spatial structure and absence of boundary detail, respectively. Along with the fusion of three branches, the coarse segmentation results are gradually refined in structure details and boundary quality. Without adding additional learnable parameters, we further propose Scale-Adaptive Pooling Module to obtain multi-scale receptive filed. In particular, on the premise of inheriting this framework, we rethink the relationship among accuracy, parameters and speed via network depth-width tradeoff. With these insightful considerations, we comprehensively design shallower and narrower models to explore the maximum potential of lightweight SOD. Our models are purposed for different application environments: 1) a tiny version CTD-S (1.7M, 125FPS) for resource constrained devices, 2) a fast version CTD-M (12.6M, 158FPS) for speed-demanding scenarios, 3) a standard version CTD-L (26.5M, 84FPS) for high-performance platforms. Extensive experiments validate the superiority of our method, which achieves better efficiency-accuracy balance across five benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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