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arXiv 2024年

作者： Fu, Jun Zhou, Wei Jiang, Qiuping Liu, Hantao Zhai, Guangtao The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom The School of Information Science and Engineering Ningbo University Ningbo315211 China The Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China

Recently, textual prompt tuning has shown inspirational performance in adapting Contrastive Language-Image Pre-training (CLIP) models to natural image quality assessment. However, such uni-modal prompt learning method only tunes the language branch of CLIP models. This is not enough for adapting CLIP models to AI generated image quality assessment (AGIQA) since AGIs visually differ from natural images. In addition, the consistency between AGIs and user input text prompts, which correlates with the perceptual quality of AGIs, is not investigated to guide AGIQA. In this letter, we propose vision-language consistency guided multi-modal prompt learning for blind AGIQA, dubbed CLIP-AGIQA. Specifically, we introduce learnable textual and visual prompts in language and vision branches of CLIP models, respectively. Moreover, we design a text-to-image alignment quality prediction task, whose learned vision-language consistency knowledge is used to guide the optimization of the above multi-modal prompts. Experimental results on two public AGIQA datasets demonstrate that the proposed method outperforms state-of-the-art quality assessment models. The source code is available at https://***/JunFu1995/CLIP-AGIQA. © 2024, CC BY-NC-ND.

关键词： Learning systems

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Quantum-behaved PSO-based Lyapunov control of closed quantum systems

Quantum-behaved PSO-based Lyapunov control of closed quantum...

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第40届中国控制会议

作者： Song Liu Sen Kuang Department of Automation University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

A Lyapunov-based control scheme is presented to drive closed quantum systems into any target eigenstate with as high population as possible by the quantum-behaved particle swarm optimization（PSO） algorithm. Based on a Lyapunov function with a Hermitian operator to be constructed, a control law with the unknown parameters contained in the Hermitian operator is designed. To achieve high-population state transfer to the target state, we first initialize those unknown parameters by choosing a path to the target state in its energy-level connectivity graph and setting their values along the path. Then, a set of optimal parameters is found by the quantum-behaved PSO algorithm. Finally, numerical simulation experiments are performed on a five-level quantum system and a four-qubit system to demonstrate the effectiveness of the control scheme in this paper.

关键词： Quantum systems Lyapunov control quantum-behaved PSO algorithm energy-level connectivity graph high-population transfer

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A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography

arXiv

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arXiv 2024年

作者： Gao, Silin Wang, Wenlong Wang, Muhan Zhang, Zhe Yang, Zai Qiu, Xiaolan Zhang, Bingchen Wu, Yirong Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics & Statistics Xi’an Jiaotong University Xi’an710049 China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Suzhou215123 China China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China

Synthetic aperture radar (SAR) tomography (TomoSAR) retrieves three-dimensional (3-D) information from multiple SAR images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on 1 norm regularization and other grid-based CS methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the "off-grid" effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This paper presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vectors (MMV) model is constructed to enhance noise immunity without compromising azimuth-range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. Validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost effectiveness

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A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient SMV Gridless Line Spectrum Estimation with applications in Tomographic SAR Imaging

arXiv

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arXiv 2022年

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Jiangsu Suzhou215123 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China

This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the array aperture, or adopt the sparsity constraint and utilize compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least squares (LS) estimation problem and solves the corresponding objective function via gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, as well as performance analysis are discussed in this paper. Numerical simulations and real data experiments show that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g., CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experiment data. Results show great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis

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Rpsa: Riemannian Principal Skewness Analysis

SSRN

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SSRN 2023年

作者： Geng, Xiurui Gao, Jingyu Aerospace Information Research Institute Chinese Academy of Sciences Beijing100864 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100864 China

By transforming the determination of the principal skewness direction of data into the solution of the eigenpair (eigenvalue and eigenvector) of its coskewness tensor, the principal skewness analysis (PSA) has been introduced into the blind source separation (BSS) community. Different from the eigenpair problem of a matrix which has been extensively and thoroughly studied, the solution of the eigenpairs of a coskewness tensor is a recently emerging research topic and can only be obtained by means of optimization at present. Generally, the iteration direction and the iteration step size are the two most critical factors in optimization methods. As the iteration direction, the conventional gradient has been widely used to solve the eigenpairs of a coskewness tensor in the existing methods. However, in this paper, we have found that compared with the conventional gradient, Riemannian gradient as the search direction is more conducive to avoid the current eigenvector converging to the ones that have been obtained. In addition, in order to obtain the rest locally maximal eigenpairs of a coskewness tensor, a new step-size adjustment strategy is also designed in this study. Experiments on simulated and real data demonstrate the effectiveness and superiority of the presented method. © 2023, The Authors. All rights reserved.

关键词： Tensors

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K-Wise Multi-Graph Matching

SSRN

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SSRN 2023年

作者： Zhu, Xinwen Zhu, Liangliang Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences Beijing100864 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100864 China

Multi-graph matching, which aims to find the correspondences of multiple graphs, is an extension of conventional two-graph matching. There are mainly two categories of multi-graph matching methods, i.e., the pairwise-based methods and the tensor-based methods. Both of them try to find the correspondences between graphs by maximizing the values of the similarities between the corresponding features, e.g., nodes, edges, and so on. The difference is that for a set of features from different graphs to be matched, the pairwise-based methods consider the similarities of every two features, while the tensor-based methods directly consider the whole similarity among them. Therefore, the measurement of similarities in the tensor-based methods is usually much more flexible than that in the pairwise-based methods. However, the time and space complexities of the tensor-based methods grow geometrically as the number of graphs to be matched increases. In this paper, through a comprehensive investigation, we propose a k-wise multi-graph matching method which includes the aforementioned two kinds of methods as special cases. Generally, by choosing different value of k, it could be found that the proposed algorithm can strike a balance between flexible similarity measurement and low computational complexity. In addition, a factorization of k-wise global affinity matrix is proposed to save the computational cost of our method. Experiments with synthetic and real data demonstrate the superiority of our method. © 2023, The Authors. All rights reserved.

关键词： Tensors

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Electromagnetic Simulation Aided SAR Target Classification Via Deep Domain Adaptation

Electromagnetic Simulation Aided SAR Target Classification V...

引用

2021 CIE International Conference on Radar, Radar 2021

作者： Lv, Xiaoling Qiu, Xiaolan Yu, Wenming Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Aerospace Information Research Institute Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Suzhou China Southeast University State Key Laboratory of Millimeter Waves Nanjing China

ISBN: (纸本)9781665498142

Convolutional neural networks (CNNs) have made tremendous success in optical images classification recently. However, in synthetic aperture radar (SAR) target classification, it is difficult to annotate a large amount of real SAR images to train CNNs. Sufficient annotated images can be easily obtained through simulation, but the disparity between the simulated images and the real images makes them difficult to directly apply to the real images classification. In this paper, we propose a model that integrates multi-kernel maximum mean discrepancy (MK-MMD) and domain-adversarial training to alleviate this problem. Simulated SAR images with annotation and unlabeled real SAR images are used to train our model. First, we use domain-adversarial training to prompt the model to extract domain-invariant features. Then, the MK-MMD between the hidden representations of simulated images and real images is reduced to narrow domain discrepancy. Experimental results on the real SAR dataset demonstrate that our method effectively solves the domain shift problem and improves the classification accuracy. © 2021 IEEE.

关键词： convolutional neural network (CNN) Synthetic aperture radar (SAR) target classification unsupervised domain adaptation (UDA)

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Multi-scale Residual Alignment Transformer for Remote Sensing Image Change Detection

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IEEE geoscience and Remote Sensing Letters 2025年

作者： Li, Lei Zhou, Xiao Yu, Xiaodong Gao, Bin Yan, Guogang Liu, Yidan Cui, Tingting Zhao, Guangyu Hu, Yuxin Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application System Key Laboratory of Target Cognition and Application Technology Aerospace Information Research Institute Beijing100094 China

Deep learning (DL) methods have shown great potential for remote sensing image change detection recently, but still suffer from several limitations. Within the identical semantic concept, significant but irrelevant changes in surface texture, color and spatial shifting of building objects resulted from variations in imaging physical factors, causes feature inconsistency of building objects in bitemporal sences. Conventional DL methods lack the capability to effectively distinguish real changes from irrelevant changes, leading to some false detections. This letter propose a novel framework, the multi-scale residual alignment transformer (AlignFormer), to mitigate the above issues. Specifically, inspired by deformable attention mechanism, we firstly design an adaptive feature alignment module (AFAM) to suppress the inconsistency of feature pairs, where the regions of building can be adaptively focused on and the spatial-temporal dependencies of relevant building objects in feature pairs effectively captured, via scheme of flexibly sampling keys/Values for each given Query. Besides, we utilize an extremely tiny Swin Transformer as the backbone of differencing-based framework for obtaining hierarchical features. Moreover, inspired by residual learning strategy, three AFAMs are integrated into the framework to form the multi-scale residual architecture for the coarse-to-fine alignment of the paired features. Experimental results confirm the superiority of our proposed method over several state-of-the-art algorithms. Our code will be released at https://***/lilei-aircas/AlignFormer_CD. © 2004-2012 IEEE.

关键词： Change detection

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TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images 1

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Lin, Jianxin Pang, Yingxue Xia, Yingce Chen, Zhibo Luo, Jiebo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China University of Rochester Rochester United States

ISBN: (数字)9783030585488

ISBN: (纸本)9783030585471

An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous unpaired images from different domains for training, there are many scenarios where training data is quite limited. In this paper, we argue that even if each domain contains a single image, UI2I can still be achieved. To this end, we propose TuiGAN, a generative model that is trained on only two unpaired images and amounts to one-shot unsupervised learning. With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details. We conduct extensive experiments to verify that our versatile method can outperform strong baselines on a wide variety of UI2I tasks. Moreover, TuiGAN is capable of achieving comparable performance with the state-of-the-art UI2I models trained with sufficient data. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Robust Eigenvectors of Regular Simplex Tensors: Conjecture Proof

arXiv

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arXiv 2023年

作者： Wang, Lei Geng, Xiurui Zhang, Lei The School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of the Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Science Beijing100190 China

The concept of tensor eigenpairs has received more researches in past decades. Recent works have paid attentions to a special class of symmetric tensors termed regular simplex tensors, which is constructed by equiangular tight frame of n + 1 vectors in n-dimensional space, and the robustness of eigenpairs was investigated. In the end of the literature, a conjecture was claimed that the robust eigenvectors of a regular simplex tensor are precisely the vectors in the frame. One later work theoretically proved that the case of n = 2 was true. In this paper, we proceed further and complete the proof for the above conjecture. Some promising directions are discussed in the end for future *** Codes 15A69, 90C26 © 2023, CC BY.

关键词： Tensors

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