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Proceedings of the 2023 5th International Conference on Video, Signal and Image processing

作者： Ting Liu Yong Wang Jianhong Xiang Ying Wang Yang Liu College of Information and Communication Engineering Harbin Engineering University China Sichuan Key Laboratory of Agile Intelligent Computing Southwest China Institute of Electronics Technology China

ISBN: (纸本)9798400709272

Millimeter-wave (mmWave) and massive Multiple Input Multiple Output (MIMO) technologies rely heavily on accurate channel state information (CSI) to significantly increase system capacity. However, the large size of the antenna array makes it more difficult to obtain accurate channel state information. We propose to utilize beam domain channels to improve the channel estimation accuracy. Specifically, we first construct the angular domain compensation. By utilizing this angular domain compensation, we further propose an angular domain compensated alternating direction multiplier method (ADC-ADMM) algorithm. The scheme models the channel estimation problem as a bi-objective convex optimization problem with joint angular domain compensation, which can capture channel state information more comprehensively. In addition, the angular domain compensation can provide a priori information and improve the robustness in noisy environments. The simulation results confirm the feasibility of the proposed ADC-ADMM scheme.

关键词： ADC-ADMM Millimeter wave (mmWave) Multiple Input Multiple Output (MIMO) channel estimation

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation

arXiv

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arXiv 2024年

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm’s generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024, CC BY.

关键词： Semantic Segmentation

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Low-rank matrix recovery with total generalized variation for defending adversarial examples

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Frontiers of information technology & Electronic Engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

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Contrastive Learning of Person-independent Representations for Facial Action Unit Detection

arXiv

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arXiv 2024年

作者： Li, Yong Shan, Shiguang The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China The University of Chinese Academy of Sciences Beijing100049 China Peng Cheng Laboratory Shenzhen518055 China

Facial action unit (AU) detection, aiming to classify AU present in the facial image, has long suffered from insufficient AU annotations. In this paper, we aim to mitigate this data scarcity issue by learning AU representations from a large number of unlabelled facial videos in a contrastive learning paradigm. We formulate the self-supervised AU representation learning signals in two-fold: (1) AU representation should be frame-wisely discriminative within a short video clip;(2) Facial frames sampled from different identities but show analogous facial AUs should have consistent AU representations. As to achieve these goals, we propose to contrastively learn the AU representation within a video clip and devise a cross-identity reconstruction mechanism to learn the person-independent representations. Specially, we adopt a margin-based temporal contrastive learning paradigm to perceive the temporal AU coherence and evolution characteristics within a clip that consists of consecutive input facial frames. Moreover, the cross-identity reconstruction mechanism facilitates pushing the faces from different identities but show analogous AUs close in the latent embedding space. Experimental results on three public AU datasets demonstrate that the learned AU representation is discriminative for AU detection. Our method outperforms other contrastive learning methods and significantly closes the performance gap between the self-supervised and supervised AU detection approaches. © 2024, CC BY.

关键词： Learning systems

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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Rich-text document styling restoration via reinforcement learning

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Frontiers of Computer Science 2021年第4期15卷 93-103页

作者： Hongwei LI Yingpeng HU Yixuan CAO Ganbin ZHOU Ping LUO Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Search Product Center WeChat Search Application DepartmentTencentBeijing 100080China

Richly formatted documents,such as financial disclosures,scientific articles,government regulations,widely exist on ***,since most of these documents are only for public reading,the styling information inside them is usually missing,making them improper or even burdensome to be displayed and edited in different formats and *** this study we formulate the task of document styling restoration as an optimization problem,which aims to identify the styling settings on the document elements,e.g.,lines,table cells,text,so that rendering with the output styling settings results in a document,where each element inside it holds the(closely)exact position with the one in the original *** that each styling setting is a decision,this problem can be transformed as a multi-step decision-making task over all the document elements,and then be solved by reinforcement ***,Monte-Carlo Tree Search(MCTS)is leveraged to explore the different styling settings,and the policy function is learnt under the supervision of the delayed *** a case study,we restore the styling information inside tables,where structural and functional data in the documents are usually *** shows that,our best reinforcement method successfully restores the stylings in 87.65%of the tables,with 25.75%absolute improvement over the *** also discuss the tradeoff between the inference time and restoration success rate,and argue that although the reinforcement methods cannot be used in real-time scenarios,it is suitable for the offline tasks with high-quality ***,this model has been applied in a PDF parser to support cross-format display.

关键词： styling restoration monte-carlo tree search reinforcement learning richly formatted documents tables

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Current Problems and Solutions of Industrial Control Network Intrusion Detection: A Brief Survey

Current Problems and Solutions of Industrial Control Network...

引用

2022 IEEE International Conference on Real-Time computing and Robotics, RCAR 2022

作者： Jiang, Tianyu Zhang, Chen Wang, Rui Chai, Senchun Institute of Automation Chinese Academy of Sciences and University of Science and Technology Beijing China Beijing Institute of Technology China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems China Institute of Intelligent Information Processing and Control Beijing Institute of Technology China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the advancement of industrial intelligence, the Industrial Internet has been widely used in energy, manufacturing and other important industries. In recent years, security incidents have occurred frequently in industrial control network, which means that maintaining industrial control network security has become more and more important. Intrusion detection technology can actively detect abnormal behavior in the network, and is an important means to ensure the security of industrial control networks. Presently, the intrusion detection technology of industrial control network has problems such as extreme imbalance of classes and redundant interference of traffic characteristics. This paper analyzes these problems and clarifies the existing solutions for different problems in principle. © 2022 IEEE.

关键词： Network security

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Disentangle Nighttime Lens Flares: Self-supervised Generation-based Lens Flare Removal

arXiv

引用

arXiv 2025年

作者： He, Yuwen Wang, Wei Wang, Wanyu Jiang, Kui Wu, Wanyu Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Harbin Institute of Technology China

Lens flares arise from light reflection and refraction within sensor arrays, whose diverse types include glow, veiling glare, reflective flare and so on. Existing methods are specialized for one specific type only, and overlook the simultaneous occurrence of multiple typed lens flares, which is common in the real-world, e.g. coexistence of glow and displacement reflections from the same light source. These co-occurring lens flares cannot be effectively resolved by the simple combination of individual flare removal methods, since these coexisting flares originates from the same light source and are generated simultaneously within the same sensor array, exhibit a complex interdependence rather than simple additive relation. To model this interdependent flares’ relationship, our Nighttime Lens Flare Formation model is the first attempt to learn the intrinsic physical relationship between flares on the imaging plane. Building on this physical model, we introduce a solution to this joint flare removal task named Self-supervised Generation-based Lens Flare Removal Network (SGLFR-Net), which is self-supervised without pre-training. Specifically, the nighttime glow is detangled in PSF Rendering Network(PSFR-Net) based on PSF Rendering Prior, while the reflective flare is modelled in Texture Prior Based Reflection Flare Removal Network (TPRR-Net). Empirical evaluations demonstrate the effectiveness of the proposed method in both joint and individual glare removal tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Glare

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models

Evolutionary Multi-Objective Neural Architecture Search for ...

引用

Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Shangshang Yang Cheng Zhen Ye Tian Haiping Ma Yuanchao Liu Panpan Zhang Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.

关键词：

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Specific Emitter Identification Based on ACGAN and STFT

Specific Emitter Identification Based on ACGAN and STFT

引用

Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Bin Wang Jun Xie Feng Wang The 38th Research Institute of China Electronics Technology Group Corporation Key Laboratory of Aperture Array and Space Application Key Laboratory of Intelligent Information Processing Hefei Anhui China School of Computer and Information Hefei University of Technology Hefei China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Deep learning methodologies are gaining popularity for enhancing the efficiency of Specific Emitter Identification (SEI). Nevertheless, the lack of labeled datasets continues to pose a challenge in most non-cooperative scenarios for SEI. Addressing this challenge, a framework for SEI is proposed, leveraging Short-Time Fourier Transform (STFT) in conjunction with auxiliary classifier GAN(ACGAN). The Short-Time Fourier Transform of emitters is utilized as radio-frequency fingerprint for the emitters. The input latent layer is infused with label information to steer the training process of the Generative Adversarial Network. Through semi-supervised training, shared weight updates are implemented for the classification network. Experimental results demonstrate that our model can achieve superior or competitive performances in several datasets compared with the former models.

关键词： Training Radio frequency Deep learning Fourier transforms Control engineering Fingerprint recognition Generative adversarial networks

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