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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yihao Yang, Meng Cao, Rui School of Computer Science and Engineering Sun Yat-Sen University Guangdong Guangzhou China State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi'an China Ministry of Education Guangdong Guangzhou China School of Information Science and Technology State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest University Shaanxi Xi'an China

ISBN: (纸本)9798400706868

Addressing the disparity in description granularity and information gap between images and text has long been a formidable challenge in text-based person retrieval (TBPR) tasks. Recent researchers tried to solve this problem by random local alignment. However, they failed to capture the fine-grained relationships between images and text, so the information and modality gaps remain on the table. We align image regions and text phrases at the same semantic granularity to address the semantic atomicity gap. Our idea is first to extract and then exploit the relationships between fine-grained locals. We introduce a novel Fine-grained Semantic Alignment with Transferred Person-SAM (SAP-SAM) approach. By distilling and transferring knowledge, we propose a Person-SAM model to extract fine-grained semantic concepts at the same granularity from images and texts of TBPR and its relationships. With the extracted knowledge, we optimize the fine-grained matching via Explicit Local Concept Alignment and Attentive Cross-modal Decoding to discriminate fine-grained image and text features at the same granularity level and represent the important semantic concepts from both modalities, effectively alleviating the granularity and information gaps. We evaluate our proposed approach on three popular TBPR datasets, demonstrating that SAP-SAM achieves state-of-the-art results and underscores the effectiveness of end-to-end fine-grained local alignment in TBPR tasks. © 2024 ACM.

关键词： Visual languages

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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A Self-Powered Tactile Sensing Interface For Human-Machine Interaction 22

A Self-Powered Tactile Sensing Interface For Human-Machine I...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Sun, Yuyang Li, Hanyang Wang, Kaiyao Feng, Xiaowei Hou, Cheng Chen, Tao Liu, Huicong Soochow University School Of Mechanical And Electrical Engineering Jiangsu Provincial Key Laboratory Of Advanced Robot Suzhou215123 China Soochow University School Of Computer Science And Technology Suzhou215123 China

ISBN: (纸本)9784886864352

Human-machine interfaces (HMIs) are the critical platforms for achieving effective and intuitive operations and control tasks between the user and machine. In this work, we propose a self-powered tactile sensing interface (STI) based on an array of bilayer single-electrode triboelectric nanogenerator (TENG) units. For each TENG unit, a pair of innovative, flexible materials, i.e., PET-PDMS and PTFE-PDMS, are arranged in a mosaic pattern, which can serialize the signal peaks and valleys, thus realizing the encoding of tactile sensing information and optimization of the wiring layout. This self-powered, signal-serialized and neatly wired STI can demonstrate the control of intelligent vehicles by detecting touch events. © 2023 IEEJ.

关键词： Human-machine interface self-powered signal-serializing triboelectric nanogenerator

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A Model Inference Attack Based on Random Sampling in DLaaS 24th

A Model Inference Attack Based on Random Sampling in DLaaS

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Feng Sun, Shouyue Yang, Jiaxun Wu, Liwen Cui, Lei Qu, Youyang Yao, Shaowen Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Engineering Research Center of Cyberspace Yunnan University Kunming China School of Information Science and Engineering Yunnan University Kunming China Data61 Commonwealth Scientific and Industrial Research Organization BurwoodVIC3166 Australia

ISBN: (纸本)9789819615506

Deep learning as a service (DLaaS) has become an effective business solution in numerous domains. Nevertheless, its service form has been proven vulnerable to model inference by previous research. Existing work on model inference attack relies on strong assumptions, such as the attacker knows the sample variance and model structure, which may not thoroughly reflect the attack’s potential threats. In this paper, we propose MC-infer, a zero-knowledge, real-data-free, and black-box model inference method inspired by Monte-Carlo sampling. In particular, MC-infer feeds random noises obtained from different distributions to the target model, and sniffs the corresponding target distributions according to its feedback. Then many samples are taken in these distributions to obtain enough robust noises. Finally, the distribution represented by the target model is fitted through these noises to perform model inference. Our extensive evaluations demonstrate that MC-infer can effectively infer the target model with less information, and general noise perturbation on the model’s outputs cannot defend against MC-infer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent systems

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Port-controllable routing of orbital angular momentum modes using a rotatable diffractive neural network

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science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 154-163页

作者： Junmin Liu Jiafu Chen Qingji Zeng Zemin Liang Xinping Wu Xin Zhao Jiangnan Xiao Huapeng Ye Ze Dong Dianyuan Fan Shuqing Chen Institute of Microscale Optoelectronics Shenzhen UniversityShenzhen 518060China College of New Materials and New Energies Shenzhen Technology UniversityShenzhen 518118China Terahertz Technology Innovation Research Institute Shanghai Key Laboratory of Modern Optical SystemTerahertz Science Cooperative Innovation CenterSchool of Optical-Electrical Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology&Institute of Electronic Paper Displays South China Academy of Advanced OptoelectronicsSouth China Normal UniversityGuangzhou 510006China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China

Orbital angular momentum(OAM)modes provide an additional orthogonal physical dimension,offering transformative potential for enhancing optical communication *** significant progress in mode multiplexing,the development of robust communication networks faces persistent challenges,particularly in effectively routing and controlling these multiplexed channels among network *** tackle these dilemmas,we propose a rotatable diffractive neural network(R-DNN)strategy and demonstrate its capability for port-controllable OAM mode *** leveraging the correlation between the orthogonal evolution of OAM modes in free space and phase modulations during propagation,the R-DNN precisely shapes the spatial evolution of mode fields through multiple rotatable phase layers,enabling efficient routing to specific output *** approach exploits the interaction of secondary wavelets with the relative states of the rotatable layers,allowing on-demand control of mode evolution paths and enhancing routing *** a proof of concept,we developed a tri-functional router that successfully directs three OAM modes to individually controllable output *** router achieves an average intermode crosstalk of less than−16.4 dB across three functional states,one-dimensional,two-dimensional,and cross-connected switching,while supporting the routing of 5.85 Tbit/s quadrature phase-shift keying *** results highlight the R-DNN’s effectiveness in achieving precise and controllable OAM mode manipulation,paving the way for advanced applications in mode-multiplexed communication networks and beyond.

关键词： orbital angular momentum mode mode routing diffractive neural network

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Unsupervised Nonlinear Adaptive Manifold Learning for Global and Local Information

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Tsinghua science and technology 2021年第2期26卷 163-171页

作者： Jiajun Gao Fanzhang Li Bangjun Wang Helan Liang School of Computer Science and Technology Soochow UniversitySuzhou 215006China Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhou 215006China

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local *** this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology *** method aims at minimizing global pairwise data distance errors as well as local structural *** order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear ***,we add a feature approximate error that can be used to learn a linear *** addition,we use a method of adaptive neighbor selection to calculate local structural *** paper uses the kernel matrix method to optimize the original *** algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

关键词： unsupervised manifold learning global and local information,adaptive neighbor selection method kernel matrix

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Accurate Parameter Estimation Based on the Two-Sided KZ Filter in Low Signal-to-Noise Ratio Conditions 6

Accurate Parameter Estimation Based on the Two-Sided KZ Filt...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Zhang, Liang Wang, Qing-Guo Hong Kong Baptist University Department of Computer Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Institute of AI and Future Networks Beijing Normal University Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798331541699

System identification is the process of developing models based on data collected from dynamic systems. A significant challenge in this field is accurately estimating model parameters using input-output data. This data, typically gathered through sensors, often contains noise due to inaccuracies in the sensors and errors during communication. The presence of such noise can lead to biased estimates of parameters, which undermines the model’s effectiveness. While traditional methods strive to minimize this bias during parameter estimation, they frequently encounter difficulties when dealing with high levels of noise. An alternative strategy involves employing filtering techniques to clean the data before estimation takes place. Low-pass (LP) filters are designed to reduce frequencies above a specific threshold and are widely used for mitigating noise issues. Among these filters, the Kolmogorov–Zurbenko (KZ) filter operates as multiple iterations of a moving average (MA) filter and has shown strong capabilities in reducing noise;it is commonly applied in high-resolution signal analysis and image processing tasks. In this study, we introduce two KZ filtering approaches aimed at denoising data prior to parameter estimation. Theoretical evaluations and experimental findings indicate that the proposed KZ filtering method surpasses LP filters and wavelet denoising techniques, particularly in environments characterized by low Signal-to-Noise Ratios (SNR). © 2024 IEEE.

关键词： Low pass filters

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The Immense Impact of Reverse Edges on Large Hierarchical networks

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Engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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MC$^{2}$2LS: Towards Efficient Collective Location Selection in Competition

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IEEE Transactions on Knowledge and Data Engineering 2024年第2期37卷 766-780页

作者： Meng Wang Mengfei Zhao Hui Li Jiangtao Cui Bo Yang Tao Xue School of Computer Science & The Shaanxi Key Laboratory of Clothing Intelligence Xi’an Polytechnic University Xi’an China School of Computer Science and Technology Xidian University Xi’an China Shanghai Yunxi Technology Shanghai China

Collective Location Selection (CLS) has received significant research attention in the spatial database community due to its wide range of applications. The CLS problem selects a group of k preferred locations among candidate sites to establish facilities, aimed at collectively attracting the maximum number of users. Existing studies commonly assume every user is located in a fixed position, without considering the competition between peer facilities. Unfortunately, in real markets, users are mobile and choose to patronize from a host of competitors, making traditional techniques unavailable. To this end, this paper presents the first effort on a CLS problem in competition scenarios, called mc $^{2}$ 2 ls , taking into account the mobility factor. Solving mc $^{2}$ 2 ls is a non-trivial task due to its NP-hardness. To overcome the challenge of pruning multi-point users with highly overlapped minimum boundary rectangles (MBRs), we exploit a position count threshold and design two square-based pruning rules. We introduce IQuad-tree, a user-MBR-free index, to benefit the hierarchical and batch-wise properties of the pruning rules. We propose an $(1-\frac{1}{e})$ -approximate greedy solution to mc $^{2}$ 2 ls and incorporate a candidate-pruning strategy to further accelerate the computation for handling skewed datasets. Extensive experiments are conducted on real datasets, demonstrating the superiority of our proposed pruning rules and solution compared to the state-of-the-art techniques.

关键词： Object oriented modeling Spatial databases Position measurement Companies Turning Strategic planning Spatial indexes Public transportation Peer-to-peer computing Logic

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Learning Deep RGBT Representations for Robust Person Re-identification

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International Journal of Automation and computing 2021年第3期18卷 443-456页

作者： Ai-Hua Zheng Zi-Han Chen Cheng-Long Li Jin Tang Bin Luo Anhui Provincial Key Laboratory of Multi-modal Cognitive Computation School of Computer Science and TechnologyAnhui UniversityHefei 230601China

Person re-identification(Re-ID)is the scientific task of finding specific person images of a person in a non-overlapping camera networks,and has achieved many breakthroughs ***,it remains very challenging in adverse environmental conditions,especially in dark areas or at nighttime due to the imaging limitations of a single visible light *** handle this problem,we propose a novel deep red green blue(RGB)-thermal(RGBT)representation learning framework for a single modality RGB person *** to the lack of thermal data in prevalent RGB Re-ID datasets,we propose to use the generative adversarial network to translate labeled RGB images of person to thermal infrared ones,trained on existing RGBT *** labeled RGB images and the synthetic thermal images make up a labeled RGBT training set,and we propose a cross-modal attention network to learn effective RGBT representations for person Re-ID in day and night by leveraging the complementary advantages of RGB and thermal *** experiments on Market1501,CUHK03 and Duke MTMC-re ID datasets demonstrate the effectiveness of our method,which achieves stateof-the-art performance on all above person Re-ID datasets.

关键词： Person re-identification(Re-ID) thermal infrared generative networks attention deep learning

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