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Text-image Generation Using Visual-Language Transformer parallel Decoding 9

Text-Image Generation Using Visual-Language Transformer Para...

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9th International conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Fang, Peng Wang, Fan Chongqing Three Gorges University School of Electronic and Information Engineering Chongqing China

ISBN: (纸本)9798350376548

Autoregressive generative models have shown their superiority in the fields of vision and language, such as high accuracy, high fidelity, and strong stability. Most existing methods are trained around text-guided image generation tasks and image-guided text generation tasks. However, not only can these two generative tasks be trained on the same dataset, but both require the alignment of visual and textual representations in the same semantic space. In order to be able to learn better generalization of image and text representations, we unify these two generation tasks in an autoregressive generative model and enhance the quality of text and image generation through bidirectional generation goals. In order to take the 2D-structure of the image into account. To this end, we propose a visual-linguistic Transformer autoregressive generative model. The visual-language Transformer model adopts a novel decoding strategy that takes the 2D-structure of the image into account without affecting the generation of the 1D sequence structure of the text. Through improved training strategies, we can not only generate highly accurate text descriptions, but also enhance the model's image understanding and generate high-quality images. © 2024 IEEE.

关键词： Decoding

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution image inpainting Underwater images

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DTPFN: End-to-End Multi-Source Fusion Detection Network for Robust Performance in Harsh Environments 24

DTPFN: End-to-End Multi-Source Fusion Detection Network for ...

引用

4th International conference on Signal processing and Communication Technology, SPCT 2024

作者： Li, Xinliang Sheng, Weixing Zhu, Hairui School of Electronic and Optical Engineering Nanjing University of Science and Technology Jiangsu Nanjing China School of Communication and Artificial Intelligence School of Integrated Circuits Nanjing Institute of Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710636

For fields with high requirements under harsh environments, such as military strikes, defense, and disaster rescue, the performance of traditional single-source image detection methods drops significantly. Moreover, the multi-source image fusion methods are structurally redundant for detection tasks and have a limited scope of application. In this paper, we put forward an end-to-end multi-source fusion detection network. By utilizing the Dual-Source Three-Branch parallel Fusion Network (DTPFN), we achieve full fusion of multi-source features and obtain excellent performance in various environments. Our experimental findings demonstrate that even in the presence of interference within a single source, our proposed approach can still retain a relatively high level of detection accuracy. Additionally, it exhibits remarkable performance in terms of both resource consumption and detection speed. Through ablation experiments, we further verify the effectiveness of each module and especially prove the crucial impact of the complementary features of DTPFN on detection performance. The DTPFN can not only fully fuse the features but also be conveniently extended, exhibiting strong potential and holding broad prospects for practical applications. © 2024 Copyright held by the owner/author(s).

关键词： image fusion

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A Dual Network Approach with Enhanced Feature Guidance for SAR-to-Optical image Translation 2

A Dual Network Approach with Enhanced Feature Guidance for S...

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

作者： He, Jingfei Mao, Yongfei Zhao, Liangbo Cui, Zihan Wang, Yu Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China China Academy of Space Technology Beijing Institute of Space Mechanics and Electricity Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images own dominant merits, such as all-weather and all-day working conditions, but it is difficult for unprofessional people to interpret SAR images. Translating SAR images into optical images to assist interpretation can facilitate the transformation of SAR data into usable information. Therefore, an advanced SAR to Optical (S2O) image translation method utilizing a parallel Generative Adversarial Network (GAN) framework was proposed. This method incorporates an optical image reconstruction network alongside the S2O translation network, enhancing the fidelity and color consistency of the translated images. By employing a domain alignment module and a deep supervision feature loss module, the network effectively utilizes abundant optical image features to overcome the scarcity of SAR-optical image pairs. Experiments conducted on the SEN1-2 dataset demonstrate superior performance over existing methods, particularly in preserving detailed structural information in images. © 2024 IEEE.

关键词： Generative adversarial networks

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Privacy-Preserving in Medical image Analysis: A Review of methods and Applications 25th

Privacy-Preserving in Medical Image Analysis: A Review of ...

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25th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zhu, Yanming Yin, Xuefei Wee-Chung Liew, Alan Tian, Hui School of Information Communication and Technology Griffith University Gold Coast4215 Australia

ISBN: (纸本)9789819642069

With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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PIOD: An Efficient parallel Iterative Algorithm for Solving Over-determined Equations 29

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Tian, Min Wei, Zhenguo Li, Yanlong Du, Wei Xiao, Lei Xu, Chaoshuai China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%. © 2023 IEEE.

关键词： conjugate gradient method MPI over-determined equations parallel algorithms parallel iterative methods

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Twin Branch Transformers-Based Re-Ranking for image Retrieval 21

Twin Branch Transformers-Based Re-Ranking for Image Retrieva...

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21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and Networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Ziqi Wang, Wei Institute of Information Engineering Chinese Academy of Sciences Beijing China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen China

ISBN: (纸本)9798350329223

image retrieval as an essential task in computer vision aims to search images to match a given query image, which could be solved with a two-stage model: retrieving a series of candidate images with a global image descriptor;re-ranking the nearest images with local descriptors. Recent study indicates that Transformer technique could reduce the number of local descriptors for the re-ranking process, and enable the model to be in parallel. However, there still exist some problems: different input orders of an image pair will produce different outputs (non-symmetric);the mean aggregation module fails to find out the difference between two images. To this end, we propose a twin branch Transformers-based re-ranking approach (TBT-RR). Specifically, we utilize a cross-attention module with shared weights and segment embeddings to make the model symmetrical. Then, we design a learnable aggregation module to adaptively take the average or difference between two images, so that local descriptors could quickly reach an agreement on whether the two images are matched. To output similarity scores between two images, we design a similarity estimation pipeline with a one-way cross-attention module. Finally, we train TBT-RR on the Google Landmark dataset and test it on the Revisited Oxford and Revisited Paris datasets. The experimental results show that the retrieval results of TBT-RR outperform previous re-ranking approaches. Besides, with less memory usage and parameter scale, TBT-RR has 50% increase in the retrieval speed and 40% reduction in the computational complexity. © 2023 IEEE.

关键词： image retrieval

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Encrypted image Retrieval Scheme Based on Neural Network and LBP Mapping 29

Encrypted Image Retrieval Scheme Based on Neural Network and...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Lan Wang, Tian Wang, Zheng Jiangsu College of Finance and Accounting School of Ie Lianyungang222061 China

ISBN: (纸本)9798350330717

The privacy-preserving image retrieval research has been a hot issue for cyber-physical system security. image encryption could effectively meet the growing privacy needs of users. However, the existing searchable encryption schemes are computationally complex, time-consuming, and have low retrieval accuracy. What's more, the image feature extraction requires multiple round communications between the cloud server and the image owner, which causes additional communication burdens. To solve these problems, a privacy-preserving image retrieval scheme combining deep learning technology and Local Binary Pattern (LBP) features is proposed. To protect images, block permutation, order-preserving encryption, and intra-block pixel substitution with multiple tables are used. The encrypted images would be uploaded to the cloud server and the LBP maps of encrypted images were calculated on the cloud side. Then, the neural network was trained by the encrypted LBP maps, and the image features were extracted from them with the trained neural network. The extracted features establish the image index. The Manhattan distance of features was utilized to measure the similarity between the stored images. Finally, the images similar to the query image were returned to the image owner. The security analysis and experimental results show that the proposed scheme is better than most existing schemes regarding security and retrieval accuracy. © 2023 IEEE.

关键词： Computation outsourcing cyber-physical system local binary pattern searchable encryption secure feature extraction

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PCU-Net: An enhanced U-network by combining PPM and CBAM for Medical image Segmentation 29

PCU-Net: An enhanced U-network by combining PPM and CBAM for...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Chen, Hejian Fu, Zhongming Liu, Li He, Mengsi Deng, Jiayi Tang, Zhuo University of South China School of Computer Science and Technology Hunan Hengyang421001 China Hunan University National Supercomputing Center School of Information Science and Engineering Hunan Changsha410082 China

ISBN: (纸本)9798350330717

U-network is a kind of full convolutional neural network, which is widely used in the field of medical image segmentation. However, it still has the problem of small targets being unable to be segmented and resulting in unsatisfactory segmentation effect. In order to solve this problem, this paper proposes an enhanced U-network by combining pyramid pooling module (PPM) and convolutional block attention module (CBAM). It's whole network is U-Net architecture, where PPM with bin sizes of 1×1, 2×2 and 3×3 are used in the downsampling part of the network, which can extract input image features of various dimensions. And CBAM is used in the downsampling part of the network, which combines convolution and attention mechanism, and can pay attention to the image from two aspects of space and channel to improve the segmentation ability of the network. Experimental results show that our network outperforms traditional Ushaped segmentation networks by 30% to 40% in metrics IoU, MAE, and Dice, respectively. © 2023 IEEE.

关键词： convolutional block attention module medical image segmentation pyramid pooling module U-network

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Dynamic Bio-Information image Recognition of Cancel Cell Death based on parallel Multiple Artificial Intelligence Modules 27

Dynamic Bio-Information Image Recognition of Cancel Cell Dea...

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27th International conference on Advanced Communications Technology, ICACT 2025

作者： Jian, Ming-Shen Ye, Yi-Ling Chung, Wen-Yu Li, Wen-Xuan Lin, Yan-Ting Huang, Bo-Yuan Sikandar, Javeria Chang, Yu-Jung CCIS Lab. Dept. of CSIE National Formosa University Yunlin632 Taiwan Dept. of Biotechnology National Formosa University Yunlin632 Taiwan

ISBN: (纸本)9791188428137

This investigation purpose a unique Dynamic Bio-Information image Recognition System (DBIRS) built for the real-time monitoring of cancer cell apoptosis produced by available medical therapy. Utilizing advances in microscopy, AI-based image recognition, and parallel computing across distributed edge devices, the proposed system dramatically boosts the precision and efficiency of cellular investigation. By deploying numerous AI modules adapted to distinct cellular states, the system provides automatic, high-accuracy recognition of morphological changes in cancer cells, hence minimizing the need on manual procedures. This technique offers remote monitoring and fast data processing via cloud integration, permitting continuous observation and timely intervention. The system's efficiency is proven through trials on K-562 cells, where it attained a recognition accuracy of 97.41%. These outcomes underline the potential of the proposed method to refine cytotoxicity assays, delivering a strong tool for increasing the efficacy and safety of immune cell treatments in clinical and research settings. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Diseases

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