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COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection

science China(Information sciences) 2025年第1期68卷 189-203页

作者： Xiaoqin ZHANG Zhenni YU Li ZHAO Deng-Ping FAN Guobao XIAO Zhejiang Province Key Laboratory of Intelligent Informatics for Safety and Emergency Wenzhou University Nankai International Advanced Research Institute (SHENZHEN FUTIAN) College of Computer Science Nankai University School of Computer Science and Technology Tongji University

We rethink the segment anything model(SAM) and propose a novel multiprompt network called COMPrompter for camouflaged object detection(COD). SAM has zero-shot generalization ability beyond other models and can provide an ideal framework for COD. Our network aims to enhance the single prompt strategy in SAM to a multiprompt strategy. To achieve this, we propose an edge gradient extraction module, which generates a mask containing gradient information regarding the boundaries of camouflaged objects. This gradient mask is then used as a novel boundary prompt, enhancing the segmentation process. Thereafter, we design a box-boundary mutual guidance module, which fosters more precise and comprehensive feature extraction via mutual guidance between a boundary prompt and a box prompt. This collaboration enhances the model's ability to accurately detect camouflaged objects. Moreover, we employ the discrete wavelet transform to extract high-frequency features from image embeddings. The high-frequency features serve as a supplementary component to the multiprompt ***, our COMPrompter guides the network to achieve enhanced segmentation results, thereby advancing the development of SAM in terms of COD. Experimental results across COD benchmarks demonstrate that COMPrompter achieves a cutting-edge performance, surpassing the current leading model by an average positive metric of 2.2% in COD10K. In the specific application of COD, the experimental results in polyp segmentation show that our model is superior to top-tier methods as well. The code will be made available at https://***/guobaoxiao/COMPrompter.

关键词： segment anything model camouflaged object detection boundary prompt

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Research on Stock Price Prediction Method Based on the GAN-LSTM-Attention Model

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computers, Materials & Continua 2025年第1期82卷 609-625页

作者： Peng Li Yanrui Wei Lili Yin College of Computer Science and Technology Harbin University of Science and TechnologyHarbin150006China

Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and *** paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price ***,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock *** discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock ***,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation *** results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.

关键词： Stock price prediction generative adversarial network attention mechanism time-series prediction

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Local Content-Aware Enhancement for Low-Light Images with Non-Uniform Illumination

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computers, Materials & Continua 2025年第3期82卷 4669-4690页

作者： Qi Mu Yuanjie Guo Xiangfu Ge Xinyue Wang Zhanli Li College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China

In low-light image enhancement,prevailing Retinex-based methods often struggle with precise illumina-tion estimation and brightness *** can result in issues such as halo artifacts,blurred edges,and diminished details in bright regions,particularly under non-uniform illumination *** propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the *** introducing multi-scale effective guided filtering,our method surpasses the limitations of traditional isotropic filters,such as Gaussian filters,in handling non-uniform *** dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates edge perception across different *** balanced approach achieves a harmonious blend of smoothing and detail preservation,enabling more accurate illumination ***,we have designed an adaptive gamma correction function that dynamically adjusts the brightness value based on local pixel intensity,further balancing enhancement effects across different brightness levels in the *** results demonstrate the effectiveness of our proposed method for non-uniform illumination images across various *** exhibits superior quality and objective evaluation scores compared to existing *** method effectively addresses potential issues that existing methods encounter when processing non-uniform illumination images,producing enhanced images with precise details and natural,vivid colors.

关键词： Retinex non-uniform low illumination local content-aware effective guided image filtering

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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computers, Materials & Continua 2025年第3期82卷 5135-5151页

作者： Jian Feng Yifan Guo Cailing Du College of Computer Science & Technology Xi’an University of Science and TechnologyXi’an710054China

Graph similarity learning aims to calculate the similarity between pairs of *** unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies,which can harm the semantic and structural information of graphs and overlook the rich structural information present in *** address these issues,we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive ***,to tackle the problem of random augmentation disrupting the semantics and structure of the graph,we design a learnable augmentation method to selectively choose nodes and edges within the *** enhance contrastive levels,we employ a biased random walk method to generate corresponding subgraphs,enriching the contrastive ***,to solve the issue of previous work not considering multi-level contrastive learning,we utilize graph convolutional networks to learn node representations of augmented views and the original graph and calculate the interaction information between the attribute-augmented and structure-augmented views and the original *** goal is to maximize node consistency between different views and learn node matching between different graphs,resulting in node-level representations for each *** representations are then obtained through pooling operations,and we conduct contrastive learning utilizing both node and subgraph ***,the graph similarity score is computed according to different downstream *** conducted three sets of experiments across eight datasets,and the results demonstrate that the proposed model effectively mitigates the issues of random augmentation damaging the original graph’s semantics and structure,as well as the insufficiency of contrastive ***,the model achieves the best overall performance.

关键词： Graph similarity learning contrastive learning attributes structure

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Symmetric-threshold ReLU for Fast and Nearly Lossless ANN-SNN Conversion

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Machine Intelligence Research 2023年第3期20卷 435-446页

作者： Jianing Han Ziming Wang Jiangrong Shen Huajin Tang College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Zhejiang Lab Hangzhou311121China

The artificial neural network-spiking neural network(ANN-SNN)conversion,as an efficient algorithm for deep SNNs training,promotes the performance of shallow SNNs,and expands the application in various ***,the existing conversion methods still face the problem of large conversion error within low conversion time *** this paper,a heuristic symmetric-threshold rectified linear unit(stReLU)activation function for ANNs is proposed,based on the intrinsically different responses between the integrate-and-fire(IF)neurons in SNNs and the activation functions in *** negative threshold in stReLU can guarantee the conversion of negative activations,and the symmetric thresholds enable positive error to offset negative error between activation value and spike firing rate,thus reducing the conversion error from ANNs to *** lossless conversion from ANNs with stReLU to SNNs is demonstrated by theoretical *** contrasting stReLU with asymmetric-threshold LeakyReLU and threshold ReLU,the effectiveness of symmetric thresholds is further *** results show that ANNs with stReLU can decrease the conversion error and achieve nearly lossless conversion based on the MNIST,Fashion-MNIST,and CIFAR10 datasets,with 6×to 250 speedup compared with other ***,the comparison of energy consumption between ANNs and SNNs indicates that this novel conversion algorithm can also significantly reduce energy consumption.

关键词： Symmetric-threshold rectified linear unit(stReLU) deep spiking neural networks artificial neural network-spiking neural network(ANN-SNN)conversion lossless conversion double thresholds

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Coordinate Descent K-means Algorithm Based on Split-Merge

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computers, Materials & Continua 2024年第12期81卷 4875-4893页

作者： Fuheng Qu Yuhang Shi Yong Yang Yating Hu Yuyao Liu College of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China College of Computer Science and Technology Jilin Agricultural UniversityChangchun130118China

The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of *** identifies better locally optimal solutions than the original K-means *** is,it achieves solutions that yield smaller objective function values than the K-means ***,CDKM is sensitive to initialization,which makes the K-means objective function values not small *** selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of *** proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value *** split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains *** algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition *** on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional *** proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.

关键词： Cluster analysis K-means coordinate descent K-means split-merge

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Recurrent Convex Difference Neural Networks for Safety-Critical Model Predictive Control

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IEEE Robotics and Automation Letters 2025年第6期10卷 6400-6407页

作者： Chen, Hanlong Wang, Yang Lin, Wang Ding, Zuohua Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou310018 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

Optimal control and planning with safety considerations constitute a fundamental challenge in model predictive control (MPC) applications, which has recently been addressed by integrating Control Barrier Functions (CBFs) to yield a safety-critical form of MPC, known as MPC-CBF. However, current neural network based approaches often face slow convergence speeds and limited prediction capabilities for MPC-CBFs. To address these limitations, we propose a Recurrent Convex Difference Neural Network (RCDiNN) framework, which can efficiently balance the prediction accuracy and convergence speed for model predictive control. It first incorporates RCDiNN to predict system dynamics and optimize control actions, and then employs a Lagrangian dual deep learning method for RCDiNN training, to encourage the satisfaction of the constraints given by MPC-CBF. We conduct an experimental evaluation on several benchmarks for obstacle avoidance, which demonstrates that our approach is more effective than the existing neural network-based MPC approaches. © 2016 IEEE.

关键词： Predictive control systems

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PiCNet: Physics-infused Convolution Network for Radar-Based Precipitation Nowcasting

PiCNet: Physics-infused Convolution Network for Radar-Based ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Zheng Zhang, Hanyi Bai, Cong College of Computer Science and Technology Zhejiang University of Technology China

ISBN: (纸本)9798350368741

Meteorological disasters, especially extreme precipitation, cause significant socioeconomic damage, highlighting the need for effective quantitative precipitation nowcasting. Existing methods, often data-driven and resource-intensive, struggle to capture the underlying physical laws of meteorology. This paper introduces a simple yet effective model using an advection simulator to learn precipitation's physical dynamics, making the predictions more interpretable. Our model also incorporates a physics-guided module to enhance sensitivity to high-intensity rainfall, improving rainfall prediction accuracy. Experiments on the KNMI radar echo dataset demonstrate that our model outperforms state-of-the-art methods, offering better insights into physics-infused precipitation nowcasting. © 2025 IEEE.

关键词： High-intensity rainfall Physics-infused method Precipitation nowcasting

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A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266000China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the College of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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