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Natural Language processing (ICNLP), International conference on

作者： Chaoqi Song Ying Liu Jinglu He Center for Image and Information Processing Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Few-shot image classification is a critical issue in the field of computer vision, facing challenges related to data scarcity and model generalization. Transformer models, representing self-attention mechanisms, have made significant strides in recent years in the domain of few-shot classification. This paper commences with an introduction to the background and challenges of few-shot classification, along with a description of the principles and structure of the Transformer model. Subsequently, the paper categorizes Transformer-based few-shot image classification methods into meta-learning-based, metric-learning-based, fine-tuning-based, and feature-enhancement-based approaches, whose theoretical foundations of each method are expounded and the comparative analysis of representative algorithms are also provided. Furthermore, the paper delves into prospective research directions in this field.

关键词： Surveys Representation learning Learning systems Computational modeling Neural networks Computer architecture Transformers Natural language processing Classification algorithms image classification

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Does progress on imageNet transfer to real-world datasets? 37

Does progress on ImageNet transfer to real-world datasets?

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37th conference on Neural Information processing systems (NeurIPS)

作者： Fang, Alex Kornblith, Simon Schmidt, Ludwig Univ Washington Seattle WA 98195 USA Google Res Brain Team Mountain View CA USA Univ Washington AI2 Seattle WA 98195 USA

ISBN: (纸本)9781713899921

Does progress on imageNet transfer to real-world datasets? We investigate this question by evaluating imageNet pre-trained models with varying accuracy (57% -83%) on six practical image classification datasets. In particular, we study datasets collected with the goal of solving real-world tasks (e.g., classifying images from camera traps or satellites), as opposed to web-scraped benchmarks collected for comparing models. On multiple datasets, models with higher imageNet accuracy do not consistently yield performance improvements. For certain tasks, interventions such as data augmentation improve performance even when architectures do not. We hope that future benchmarks will include more diverse datasets to encourage a more comprehensive approach to improving learning algorithms.

关键词： image classification

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Emerging Versatile Context Representation on Modulation Recognition with Vision Transformer 2

Emerging Versatile Context Representation on Modulation Reco...

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2nd IEEE International conference on Signal, Information and Data processing, ICSIDP 2024

作者： Chen, Baihong Rao, Bin Zou, xiaohai Wang, Wei Sun Yat-Sen University School of Electronics and Communication Engineering Shenzhen China

ISBN: (纸本)9798331515669

Accurate recognition of intra-pulse modulation patterns is essential for enhancing radar system performance. Tranditional recognition algorithms are typically designed under ideal conditions and handcrafted features, the presence of substantial noise interference in complex electromagnetic conditions can significantly degrade the performance of these algorithms. Although Transformers have achieved great success with computer vision tasks, existing vision transformers are lack of capturing long-range dependencies. To remedy these flaws, this paper proposes an accurate automatic modulation recognition algorithm based on vision transformer. First, we utilize traditional image processing techniques to provide position of signal in time-frequency image and introduce a radius basis function to generate the positional embeddings. This procedure enhances the spatial awareness of our model. Second, dynamic scales of 2D patches are embedded as the input to the encoders. Third, inspired by dilated convolutions, vanilla attention is reformed in the cascade encoders. Experiments and ablation studies exhibit the proposed algorithm performs well under conditions of various signal-to-noise ratio conditions. It is worth mentioning that the classification accuracy can be up to 93.4% when SNR is -10 dB, and reach to 94.4% when SNR is 0 dB, which indicates the proposed algorithm demonstrates superior robustness against noise perturbations compared to other methods. The present work provides a sound experimental basis for further studying automatic modulation classification for the sake of future field application in electronic warfare systems. © 2024 IEEE.

关键词： image enhancement

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Superquadric Modeling Method of 3D Object Based on Invasive Weeds Optimization and Levenberg-Marquardt algorithms 2

Superquadric Modeling Method of 3D Object Based on Invasive ...

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2nd International conference on image processing and Media Computing, ICIPMC 2023

作者： Wang, Lin Ma, Donghan Duan, Xuan Chen, Shengmei Duan, Boyue Liu, Cheng Zhang, Pu Northwest University School of Information Science and Technology Xi'an China Xi'An Mingde Institute of Technology School of Information Engineering Xi'an China

ISBN: (纸本)9798350326611

Because of the problem of insufficient parameter fitting accuracy of superquadric modeling method based on Levenberg-Marquardt (LM) algorithm, a superquadric modeling method of 3D object based on invasive weed optimization (IWO) algorithm and LM algorithm is proposed. First, according to the distance relationship between each point in point cloud and the superquadric, a nonlinear optimization model for superquadric parameter estimation of 3D object is established. Then, to obtain the initial values of superquadric fitting parameters, the IWO algorithm is adopted to optimize the optimization model. After that, LM algorithm is also adopted to optimize the optimization model, and estimation values of superquadric fitting parameters of 3D object can be obtained. Thus, the superquadric model of 3D object of the given point cloud can be established. The experimental results show that the proposed method can yield highly accurate superquadric fitting parameters, and can effectively suppress the influence of measurement noise on modeling accuracy as well. © 2023 IEEE.

关键词： Invasive weed optimization

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Generalized Single-image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer

Generalized Single-Image-Based Morphing Attack Detection Usi...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Haoyu Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Norwegian Univ Sci & Technol Trondheim Norway Darmstadt Univ Appl Sci Darmstadt Germany

ISBN: (纸本)9798350365474

Face morphing attacks have posed severe threats to Face Recognition systems (FRS), which are operated in border control and passport issuance use cases. Correspondingly, morphing attack detection algorithms (MAD) are needed to defend against such attacks. MAD approaches must be robust enough to handle unknown attacks in an open-set scenario where attacks can originate from various morphing generation algorithms, post-processing and the diversity of printers/scanners. The problem of generalization is further pronounced when the detection has to be made on a single suspected image. In this paper, we propose a generalized single-image-based MAD (S-MAD) algorithm by learning the encoding from Vision Transformer (ViT) architecture. Compared to CNN-based architectures, ViT model has the advantage on integrating local and global information and hence can be suitable to detect the morphing traces widely distributed among the face region. Extensive experiments are carried out on face morphing datasets generated using publicly available FRGC face datasets. Several state-of-the-art (SOTA) MAD algorithms, including representative ones that have been publicly evaluated, have been selected and benchmarked with our ViT-based approach. Obtained results demonstrate the improved detection performance of the proposed S-MAD method on inter-dataset testing (when different data is used for training and testing) and comparable performance on intra-dataset testing (when the same data is used for training and testing) experimental protocol.

关键词： Face Recognition Morphing Attack Detection

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image Encryption Based on Chen Chaotic System, OpenSSL S-Box and the Fibonacci Q-Matrix 27

Image Encryption Based on Chen Chaotic System, OpenSSL S-Box...

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27th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Lotfy, Rana Gabr, Mohamed Mamdouh, Eyad Aboshousha, Amr Alexan, Wassim El-Damak, Dina Fathy, Abdallah Mansour, Marvy Badr Monir German University in Cairo Faculty of MET Cairo Egypt German University in Cairo Physics Department Cairo Egypt German University in Cairo Faculty of IET Cairo Egypt ElSewedy University of Technology - Polytechnic of Egypt Faculty of Engineering Technology Department of Electronic Engineering Technology Cairo Egypt The British University in Egypt Faculty of Engineering Department of Electrical Engineering Cairo Egypt

ISBN: (纸本)9788362065486

This paper presents a novel image encryption algorithm that leverages the chaotic properties of the Chen system, the cryptographic strength of OpenSSL, and the mathematical robustness of the Fibonacci Q-Matrix. The proposed method begins by generating an encryption key using the Chen chaotic system, known for its sensitivity to initial conditions and complex dynamic behavior. This key is then utilized in conjunction with a substitution box (S box) generated through OpenSSL to introduce non-linearity and diffusion into the encryption process. To further enhance security, the resulting image data undergoes a series of multiplications by a large number of Fibonacci Q-Matrices, exploiting their recursive properties for added complexity and confusion. Numerical results demonstrate the proposed algorithm's superior performance in terms of security and efficiency, making it a promising solution for safeguarding digital images against unauthorized access and cryptographic attacks. © 2024 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Encryption algorithms

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Application of artificial intelligence algorithm model in fault diagnosis of interactive information 6

Application of artificial intelligence algorithm model in fa...

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6th International conference on image, Video processing, and Artificial Intelligence, IVPAI 2024

作者： Jin, Hulin Jin, Zhiran Kim, Yong-Guk International Institute of Engineering Psychology DenverCO80202 United States Foothill Preparatory School Temple CityCA91780 United States Department of Computer Engineering Sejong University Seoul05006 Korea Republic of

ISBN: (纸本)9781510681781

Interactive information fault diagnosis technology is a new type of fault diagnosis technology which is integrated by information fusion, artificial intelligence, computer science and other disciplines. It can extract interactive information data of equipment in real time, analyze the characteristics of fault information, and then find the change and trend of equipment operating status. However, there are still some shortcomings in the practical application of this technology, for example, it is difficult to deal with a large number of complex interactive information data, and it is difficult to carry out effective fault diagnosis. Therefore, an intelligent fault diagnosis method with improved grey relational degree is proposed in this paper. This method can effectively extract the interactive information feature data of the equipment, and carry out the correlation analysis of the fault feature data through the grey correlation analysis algorithm, which provides a new idea for the fault diagnosis of the equipment. © 2024 SPIE.

关键词： Correlation coefficients Evolutionary algorithms Matrices Artificial intelligence Fuzzy logic algorithms Data processing Algorithm development Feature extraction Intelligence systems

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Retinal vasculature image segmentation algorithm based on deep learning 2

Retinal vasculature image segmentation algorithm based on de...

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2nd Advanced Imaging and Information processing conference, AIIP 2024

作者： Jin, Nanxi xin, Yuelan Deng, Shenghui Chen, Kuan Yang, Lan Huo, Haoyi Zhao, Jia Fang, Huiting Dept. of Physics and Electronic Information Engineering Qinghai Normal University 38 Wusi West Road. Qinghai Xining810001 China State Key Laboratory of Tibetan Language Information Processing and Applications Qinghai Normal University 38 Wusi West Road. Qinghai Xining810001 China Dept. of Computer Science Qinghai Normal University 38 Wusi West Road. Qinghai Xining810001 China

ISBN: (纸本)9781510683068

To enhance the accuracy of existing algorithms in the task of retinal vessel image segmentation, this paper proposes the incorporation of two modified convolutional blocks, in lieu of traditional ones, within the framework of the U-Net neural network, aiming to strengthen the extraction of detailed features. Firstly, a convolutional block equipped with local channel attention is devised for feature extraction in the shallow layers of the network. Secondly, a convolutional block incorporating global channel attention is introduced for feature extraction in the deeper layers. Lastly, skip connections are employed to feed the features extracted from the shallow layers into the deeper layers. Experimental results demonstrate that the proposed retinal vessel segmentation algorithm achieves a Dice coefficient of 88.21% and a sensitivity of 87.16%, marking improvements of 2.25% and 1.64% respectively over the original UNet network. © 2024 SPIE.

关键词： image segmentation

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Virtual sensor design for azimuthal gamma measurement systems 6

Virtual sensor design for azimuthal gamma measurement system...

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6th International conference on Intelligent Control, Measurement and Signal processing, ICMSP 2024

作者： Zhang, Yulin Li, Fei Zhao, Wenxuan Cheng, Huaibiao Zhu, Weihong Qin, Genzhao China Oilfield Services Limited China Logging and Directional Drilling School of Electronic Engineering Directional Drilling Branch CNOOC Key Laboratory of Well Xi'an China

ISBN: (纸本)9798331516550

In response to the validation needs of the directional gamma measurement system, which require going underground or using equipment containing radiation sources, and the difficulty of conducting quantitative and qualitative debugging and verification under laboratory conditions, a virtual sensor designed for the directional gamma measurement system is proposed based on real sensor selection and attitude inversion algorithms. A testing group is also designed to verify whether the output of the virtual sensor meets the requirements. Test results indicate that the virtual sensor exhibits output characteristics consistent with those of the real sensor, enabling quantitative and qualitative validation of the directional gamma measurement system. It can replace the real sensor to assist in system development under laboratory conditions, helping to shorten the R&D cycle and reduce costs. © 2024 IEEE.

关键词： Underground equipment

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Neural Photo-Finishing

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ACM TRANSACTIONS ON GRAPHICS 2022年第6期41卷 p1-15页

作者： Tseng, Ethan Zhang, Yuxuan Jebe, Lars Zhang, Xuaner xia, Zhihao Fan, Yifei Heide, Felix Chen, Jiawen Princeton Univ Princeton NJ 08544 USA Adobe San Jose CA USA

image processing pipelines are ubiquitous and we rely on them either directly, by filtering or adjusting an image post-capture, or indirectly, as image signal processing (ISP) pipelines on broadly deployed camera systems. Used by artists, photographers, system engineers, and for downstream vision tasks, traditional image processing pipelines feature complex algorithmic branches developed over decades. Recently, image-to-image networks have made great strides in image processing, style transfer, and semantic understanding. The differentiable nature of these networks allows them to fit a large corpus of data;however, they do not allow for intuitive, fine-grained controls that photographers find in modern photo-finishing tools. This work closes that gap and presents an approach to making complex photo-finishing pipelines differentiable, allowing legacy algorithms to be trained akin to neural networks using first-order optimization methods. By concatenating tailored network proxy models of individual processing steps (e.g. white-balance, tone-mapping, color tuning), we can model a non-differentiable reference image finishing pipeline more faithfully than existing proxy image-to-image network models. We validate the method for several diverse applications, including photo and video style transfer, slider regression for commercial camera ISPs, photography-driven neural demosaicking, and adversarial photo-editing.

关键词： image processing photo-finishing raw processing

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