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Rethinking Masked Autoencoder for Salient Object Detection in Optical Remote Sensing Images From a Pseudo Image Pretraining Perspective

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Fu, Yuxiang Fang, Wei Sheng, Victor S. Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou215031 China Texas Tech University Department of Computer Science LubbockTX79409 United States

Recently masked autoencoder (MAE) has achieved great success in visual representation learning and delivered promising potential in many downstream vision tasks. However, due to the lack of a saliency supervision signal in the original MAE, almost no saliency information can be learned from the masked image reconstruction process. Therefore, salient object detection (SOD) can hardly benefit from MAE pretraining. To address this issue, we integrate the SOD model and saliency supervision in MAE and propose a simple and effective framework for dynamic hybrid masking MAE (DHMMAE), utilizing MAE with dynamic hybrid masking (DHM) ratios to pretrain SOD model. Specifically, we treat MAE as an online data augmenter to generate endless pseudo images for the SOD model to predict saliency maps, where saliency supervision is employed to ensure that the model can learn robust saliency prior knowledge from the reconstructed images. Besides, we propose a simple and novel network EANet driven by DHMMAE pretraining for SOD in optical remote sensing images (ORSIs). Two key modules are designed for EANet to further enhance its performance: the enhanced diverse feature aggregation module (EDFAM) and the adjacent-context shuffle spatial attention module (ASSAM). EDFAM aggregates diverse features via three different types of convolution layers and enhances them by convolution block attention module (CBAM). ASSAM captures spatial location information of salient objects by employing channel shuffle operation and weighted spatial attention mechanism on the fused adjacent context. Experiments on three ORSI-SOD datasets demonstrate that our proposed method outperforms the cutting-edge methods. © 1980-2012 IEEE.

关键词： Object detection

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Biparty Multiobjective Optimal Power Flow: The Problem Definition and an Evolutionary Approach

SSRN

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SSRN 2023年

作者： Chang, Yatong Luo, Wenjian Lin, Xin Song, Zhen Coello Coello, Carlos A. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Artificial Intelligence Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Departamento de Computación CINVESTAV-IPN Mexico City Mexico School of Engineering and Sciences Tecnologico de Monterrey Nuevo León Monterrey Mexico

The multiobjective optimal power flow (MOOPF) problem consists of adjusting the generator power and the voltage state of each node within the feasible range in the process of power transmission and, finally of achieving the objectives of optimizing the cost, loss and stability, etc. In the MOOPF, two key decision makers are usually involved, which are the power generation sector and the transmission sector. Thus, it is more suitable to model an MOOPF as a biparty multiobjective optimal power flow (BPMOOPF) problem. However, so far, there is no work on treating and solving the MOOPF problem from the perspective of biparty multiobjective optimization. In this paper, we propose the definition of the BPMOOPF problem as well as a novel evolutionary biparty multiobjective optimization algorithm for solving the BPMOOPF problem, which we call BPMOOPF-EA. Our experimental results show that compared with respect to two state-of-the-art algorithms (C-MOEA/D and A-NSGA-III), our proposed BPMOOPF-EA has a better performance when solving the BPMOOPF problem. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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dugMatting: Decomposed-Uncertainty-Guided Matting

arXiv

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arXiv 2023年

作者： Wu, Jiawei Zhang, Changqing Li, Zuoyong Fu, Huazhu Peng, Xi Zhou, Joey Tianyi College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Institute of High Performance Computing Agency for Science Technology and Research Singapore College of Computer Science Sichuan University Chengdu China Singapore

Cutting out an object and estimating its opacity mask, known as image matting, is a key task in image and video editing. Due to the highly ill-posed issue, additional inputs, typically user-defined trimaps or scribbles, are usually needed to reduce the uncertainty. Although effective, it is either time consuming or only suitable for experienced users who know where to place the strokes. In this work, we propose a decomposed-uncertainty-guided matting (dugMatting) algorithm, which explores the explicitly decomposed uncertainties to efficiently and effectively improve the results. Basing on the characteristic of these uncertainties, the epistemic uncertainty is reduced in the process of guiding interaction (which introduces prior knowledge), while the aleatoric uncertainty is reduced in modeling data distribution (which introduces statistics for both data and possible noise). The proposed matting framework relieves the requirement for users to determine the interaction areas by using simple and efficient labeling. Extensively quantitative and qualitative results validate that the proposed method significantly improves the original matting algorithms in terms of both efficiency and efficacy. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Invulnerability Optimization of Communication Network Based on Analog Attack Strategy

Invulnerability Optimization of Communication Network Based ...

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作者： Lingling Xia Xueli Ni Zhengjun Jing Jiayin Liu Yan Zhang Department of Computer Information and Cyber Security Jiangsu Police Institute Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department College of Computer Engineering Jiangsu University of Technology

Invulnerability of communication networks directly related to security and reliability of networks has received increasing attention in recent years. In order to enhance the destruction resisting capacity of communication network,we propose a new invulnerability optimization method based on analog attack strategy and present an improved invulnerability measure with consideration of network connectivity and construction cost. The experimental simulations are conducted to construct four different optimized communication networks using analog attack strategy with different indicators of node centrality on the initial network respectively. Results demonstrate that the improved metric used to evaluate the economic benefits of network invulnerability is reasonable, and the proposed topology optimization method can improve the invulnerability of communication network.

关键词： Invulnerability optimization Structural optimization Analog attack Communication network

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Attention Residual Network for White Blood Cell Classification with WGAN Data Augmentation

Attention Residual Network for White Blood Cell Classificati...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Meng Zhao Lingmin Jin Shenghua Teng Zuoyong Li College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9781728194202

In medicine, white blood cell (WBC) classification plays an important role in clinical diagnosis and treatment. Due to the similarity between classes and lack of training data, the precise classification of WBC is still challenging. To alleviate this problem, we propose an attention residual network for WBC image classification on the basis of data augmentation. Specifically, the attention residual network is composed of multiple attention residual blocks, an adaptive average pooling layer, and a full connection layer. The channel attention mechanism is introduced in each residual block to use the feature maps of WBC learned by a high layer to generate the attention map for a low layer. Each attention residual block also introduces depth separable convolution to extract the feature of WBC and decrease the training costs. The Wasserstein Generative adversarial network (WGAN) is used to create synthetic instances to enhance the size of training data. Experiments on two image datasets show the superiority of the proposed method over several state-of-the-art methods.

关键词： Training White blood cells Costs Convolution Training data Feature extraction Generators

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[Retracted] Optimization Algorithm of Moving Object Detection Using Multiscale Pyramid Convolutional Neural Networks

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Computational Intelligence and Neuroscience 2023年第1期2023卷

作者： Zhe Yang Ziyu Bu Yexin Pan School of Computer Science and Technology Soochow University Suzhou 215006 Chinascu.edu.tw Provincial Key Laboratory for Computer Information Processing Technology Suzhou 215006 China

Object detection and recognition is a very important topic with significant research value. This research develops an optimised model of moving target identification based on CNN to address the issues of insufficient positioning information and low target detection accuracy (convolutional neural network). In this article, the target classification information and semantic location information are obtained through the fusion of the target detection model and the depth semantic segmentation model. The classification and position portion of the target detection model is provided by the simultaneous fusion of the image features carrying various information and a pyramid structure of multiscale image features so that the matched image fusion characteristics can be used by the target detection model to detect targets of various sizes and shapes. According to experimental findings, this method’s accuracy rate is 0.941, which is 0.189 higher than that of the LSTM-NMS algorithm. Through the migration of CNN and the learning of context information, this technique has great robustness and enhances the scene adaptability of feature extraction as well as the accuracy of moving target position detection.

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Identifying key Learner on Online E-Learning Platform: An Effective Resistance Distance Approach 15th

Identifying Key Learner on Online E-Learning Platform: An Ef...

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15th International Conference on Intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of information technology, Applications and Tools, FITAT 2019

作者： Lu, Chunhua Zhang, Fuquan Li, Yunpeng School of Electronic and Information Engineering Anshun University Guizhou561000 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Computer School Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9789811397097

Teachers are never the only teacher in the class, especially in online e-learning environment. The key learner who is supposed to be more active and eager to spread knowledge and motivation to other classmates has a huge potentiality to improve the quality of teaching. However, the identification of such key learner is challenging which needs lots of human experience, especially when the contact channels between teachers and students are much more monotonous in online e-learning environment. Inspired by resistance distance theory, in this paper, we apply resistance distance and centrality into an interactive network of learners to identify key learner who can effectively motivate the whole class with discussion in e-learning platform. First, we define the terms of interactive network of learners with the node, edge, and graph. Then the distance between nodes is replaced with effective resistance distance to gain better understanding of propagation among the learners. Afterward, Closeness Centrality is utilized to measure the centrality of each learner in interactive network of learners. Experimental results show that the centrality we use can cover and depict the learners’ discussion activities well, and the key learner identified by our approach under apposite stimuli can effectively motivate the whole class’ learning performance. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Graphic methods

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CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution

arXiv

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arXiv 2022年

作者： Gao, Guangwei Xu, Zixiang Li, Juncheng Yang, Jian Zeng, Tieyong Qi, Guo-Jun The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The School of Communication & Information Engineering Shanghai University Shanghai China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China The Center for Mathematical Artificial Intelligence Department of Mathematics The Chinese University of Hong Kong Hong Kong Research Center for Industries of the Future School of Engineering Westlake University OPPO Research Seattle United States

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be available at https://***/IVIPLab/CTCNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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Transfer learning-based few-shot sample deep learning-assisted template attacks

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Physical Communication 2025年 72卷

作者： Zhiying Feng Lang Li Yu Ou Lianrui Deng College of Computer Science and Technology Hengyang Normal University Hengyang 421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang 421002 China

Deep learning-assisted template attack (DLATA) is a novel side-channel attack (SCA) method proposed by Lichao Wu at CHES2022. It utilizes a triplet network to assist template attacks (TA), avoiding the redundant training and hyperparameter tuning required in traditional DL-based SCA methods. However, the training of the triplet network requires a large number of power samples due to its unique structure. We propose a new optimization scheme, in which the transfer learning (TL) technology is used to train multiple models on several similar datasets with fewer power traces, to mitigation the problem. The approach allows us to leverage pre-trained models to product a new mode on the another target dataset by fine-tuning weights so that significantly reduce the training cost for the triplet network while maintaining attack effectiveness. We remould the structure and dimensionality of similar datasets so that the models trained on them can perform effective transfer learning for training on the target dataset. Concretely, some of parameters and features obtained from pretraining can be used directly for the target task, while the rest only require fine-tuning. Evaluation and experimental validation on the public ASCAD dataset demonstrate that our method achieves or even surpasses the performance of the original method with a 90% reduction in the training set. These findings highlight the effectiveness of the proposed TL strategy in achieving robust attack performance in low-sample training environments.

关键词： Deep learning

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless Communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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