In recent years, with the continuous progress of deep learning neural networks and the construction of large data sets, new breakthroughs in voiceprint verification technology for voice calls have been rapidly achieve...
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Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, the...
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Dark Image Enhancement (DIE) aims to improve contrast and restore details for captured images under low illumination. Currently, traditional Transformer methods have achieved significant performance in the DIE problem...
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ISBN:
(数字)9798350390155
ISBN:
(纸本)9798350390162
Dark Image Enhancement (DIE) aims to improve contrast and restore details for captured images under low illumination. Currently, traditional Transformer methods have achieved significant performance in the DIE problem; however, all-pairs correlation computation is redundant in learning key properties and restoring high-order representations. To alleviate this problem, we introduce a Channel-wise Guidance Sparse Transformer framework, namely CGSformer, which not only adaptively selects the key channel-wise representations through a threshold operator, but also keeps the most useful self-attention values for feature restoration guided by the selected information. Besides, we introduce a Bidirectional Gate Feed-Forward (BGFF) network to aggregate features to better facilitate high-quality image reconstruction. The experiments are conducted on representative datasets, showing that our CGSformer consistently achieves state-of-the-art performance on widely used benchmarks.
作者：
Zhao, YueWang, JizhiKong, LingruiSui, TongtongShandong Computer Science Center
National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Provincial Key Laboratory of Industrial Network and Information System Security Qilu University of Technology Shandong Academy of Sciences Jinan Shandong China Quancheng Laboratory
Jinan Key Laboratory of Digital Security Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan Shandong China
The advancement of 5G and mobile internet technologies has propelled the development of emerging businesses and applications, demanding higher requirements for network bandwidth and computational resources. To address...
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Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskde...
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Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.
In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupe...
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Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achie...
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Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
With the rapid development of Internet technology in recent years, the demand for security support for complex applications is becoming stronger and stronger. Intel Software Guard Extensions (Intel SGX) is created as ...
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Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compre...
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In the low light image enhancement, single exposure images contains a limited dynamic range, which hinders the restoration of contrast and texture. To address these problems, we propose a multi exposure generation and...
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ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
In the low light image enhancement, single exposure images contains a limited dynamic range, which hinders the restoration of contrast and texture. To address these problems, we propose a multi exposure generation and fusion method (MEGF) which simulates multi exposure images and perform feature fusion and enhancement on these images. First, we propose a Multi-Exposure Generation (MEG) method, which constructs the Gaussian Distribution for each exposure level based on multi exposure datasets. MEG can generate images with different exposure levels based on the constructed distribution. Then, the Perceptual Importance based Multi-Exposure Feature Enhancement (PIMEFE) block is developed to fuse the feature of generated multi exposure images using VGG-16 network. Before fusion, the generated images are input to Multi Scale Recursive Feature Enhancement (MSRFE) blocks and obtain the denoised and enhanced features. Finally, the fused feature are input to Curve Adjustment (CA) block for fine tuning and provide the color enhancement on fusion features. We propose the Multiple Exposure Recursive Fusion (MERF) block which estimates the adjusting factors for CA block. Experimental results demonstrate that our method outperforms other techniques in both subjective and objective evaluations on real and synthetic datasets.
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