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22nd ieee International symposium on Intelligent Systems and Informatics, SISY 2024

作者： Gozner, Máté Fischer, Matyas-Zsigmond Simon, Karoly Zolde, Attila Fazakas, Lorant-Tibor Babes-Bolyai University Cluj-Napoca Romania Codespring Cluj-Napoca Romania

ISBN: (纸本)9798350385601

Finding free parking spaces in large car parks is often challenging for drivers. Current solutions provide only limited information, such as the total number of available spaces, and can be costly due to the need for sensors at each parking spot. The aim is to develop a platform that serves both parking lot administrators and drivers, facilitating the easy identification of available parking spaces. An artificial intelligence-driven application logic, that uses real time image processing, combined with the virtual model of the parking lot, can determine the status of each parking space. Part of the required camera network is most likely already installed in the form of security cameras. © 2024 ieee.

关键词： artificial intelligence car park management free parking spaces parking lot management real time image processing

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Adaptive Feature Self-Attention in Spiking Neural Networks for Hyperspectral Classification

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ieee TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Li, Heng Tu, Bing Liu, Bo Li, Jun Plaza, Antonio Nanjing Univ Informat Sci & Technol Inst Opt & Elect Jiangsu Engn Res Ctr Intelligent Optoelect Sensing Sch Phys & Optoelect EngnState Key Lab Cultivat B Nanjing 210044 Jiangsu Peoples R China China Univ Geosci Fac Comp Sci Wuhan 430074 Peoples R China Univ Extremadura Escuela Politecn Dept Technol Comp & Commun Hyperspectral Comp Lab Caceres 10003 Spain

Hyperspectral image (HSI) classification is crucial for remote sensing research, while its high-dimensional features make traditional algorithms difficult to cope with. Despite the breakthroughs in deep learning, the high computational complexity and energy consumption limit its application in resource-limited environments. Spiking neural networks (SNNs), mimicking the brain's information processing with low power consumption, have emerged as a promising alternative for edge computing. However, SNNs struggle with complex tasks due to the nondifferentiability of spike signals, which complicates training and exhibits limitations in extracting deep features and modeling long-range dependencies. In this article, we propose a novel SNN framework that addresses these challenges by enhancing feature extraction and efficiently capturing dependencies in hyperspectral data. Our framework integrates an adaptive refocusing convolutional layer with a spike self-attention (SSA) mechanism. The adaptive refocusing convolutional layer employs learnable parameters to dynamically adjust the convolutional kernel's response to input spike data, improving feature representation. The adaptive refocusing convolutional layer uses learnable parameters to dynamically adjust kernel responses to input spike data, enhancing feature representation. Experimental results show that this model achieves over 96% classification accuracy in a single time step, significantly surpassing current methods and effectively solving the problem of low accuracy at short time steps in SNNs. Additionally, this framework reduces computational energy consumption by approximately 12.5x compared to similar, offering new potential for edge intelligence applications.

关键词： Hyperspectral imaging Accuracy Feature extraction Convolution computational modeling Spiking neural networks Training image classification Energy consumption Adaptation models Convolutional neural networks (CNNs) hyperspectral image (HSI) classification spike self-attention (SSA) spiking neural networks (SNNs)

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Event-Assisted Blurriness Representation Learning for Blurry image Unfolding

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ieee TRANSACTIONS ON image processing 2024年 33卷 5824-5836页

作者： Zhang, Pengyu Ju, Hao Yu, Lei He, Weihua Wang, Yaoyuan Zhang, Ziyang Xu, Qi Li, Shengming Wang, Dong Lu, Huchuan Jia, Xu Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Wuhan Univ Sch Elect & Informat Wuhan 430072 Peoples R China Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China Huawei Technol Co Ltd Adv Comp & Storage Lab Beijing 100095 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China Dalian Univ Technol Sch Innovat & Entrepreneurship Dalian 116024 Peoples R China Sch Informat & Commun Engn Sch Future Technol Dalian 116024 Peoples R China Sch Artificial Intelligence Dalian 116024 Peoples R China Dalian Univ Technol Sch Future Technol Dalian 116024 Peoples R China Dalian Univ Technol Sch Artificial Intelligence Dalian 116024 Peoples R China

The goal of blurry image deblurring and unfolding task is to recover a single sharp frame or a sequence from a blurry one. Recently, its performance is greatly improved with introduction of a bio-inspired visual sensor, event camera. Most existing event-assisted deblurring methods focus on the design of powerful network architectures and effective training strategy, while ignoring the role of blur modeling in removing various blur in dynamic scenes. In this work, we propose to implicitly model blur in an image by computing blurriness representation with an event-assisted blurriness encoder. The learning of blurriness representation is formulated as a ranking problem based on specially synthesized pairs. Blurriness-aware image unfolding is achieved by integrating blur relevant information contained in the representation into a base unfolding network. The integration is mainly realized by the proposed blurriness-guided modulation and multi-scale aggregation modules. Experiments on GOPRO and HQF datasets show favorable performance of the proposed method against state-of-the-art approaches. More results on real-world data validate its effectiveness in recovering a sequence of latent sharp frames from a blurry image.

关键词： image restoration Cameras Kernel Degradation computational modeling Neural networks Videos Representation learning Modulation Training Event camera blurry image unfolding blurriness modeling ranking-based learning

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Optimizing Chinese-to-English Translation Using Large Language Models

Optimizing Chinese-to-English Translation Using Large Langua...

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2025 ieee symposium on computational intelligence in Natural Language processing and Social Media, CI-NLPSoMe 2025

作者： Huang, Donghao Wang, Zhaoxia School of Computing and Information Systems Singapore Management University Singapore Research and Development Mastercard Singapore

ISBN: (纸本)9798331508418

The advent of Large Language Models (LLMs) has significantly advanced Chinese-to-English translation tasks. However, the translation process remains challenging due to the substantial differences in syntax, semantics, and morphology between these two languages, despite notable achievements. This paper presents a comprehensive study on Chinese-to-English translation, evaluating the performance of various LLMs. We explore a range of open-source models, from 3.5 billion to 72 billion parameters, and OpenAI's latest models, across zero-shot, few-shot, and fine-tuned learning paradigms. Our analysis assesses translation quality using the COMET metric, reliability with the Translation Completeness Ratio (TCR), and efficiency via Characters per Second (CPS). The results highlight substantial trade-offs between model size, translation accuracy, and processing speed. Larger models tend to produce higher-quality translations, whereas smaller models offer greater efficiency. Fine-tuning significantly improves the performance of open-source LLMs, surpassing few-shot learning in both translation quality and processing speed. Proprietary models like GPT-4o exhibit consistent high performance without significant gains from fine-tuning. We emphasize the potential of fine-tuning with techniques like LoRA/QLoRA to optimize the balance between translation accuracy and computational efficiency, offering valuable insights for deploying LLMs in real-world translation scenarios. © 2025 ieee.

关键词： Natural language processing systems

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Rethinking image Forgery Detection and Localization via Regression Perspective

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2025年

作者： Zhang, Li Li, Dong Zhong, Yan Zhu, Jiaying Wang, Rujing Wu, Xingyu Wang, Xue Liu, Liu Hefei Univ Technol Hefei 230026 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Peking Univ Beijing 100000 Peoples R China Hong Kong Polytech Univ PolyU Dept Data Sci & Artificial Intelligence Hong Kong Peoples R China

image Forgery Detection and Localization is rapidly advancing in the field of computer vision. Most methods locate forged regions in the form of segmentation and subsequently perform detection, facing challenges such as false detections (i.e., FPs) and inaccurate boundaries. In this work, we suggest rethinking the image Forgery Detection and Localization (IFDL) task from a regression perspective and propose the CatmullRom Splines-based Regression Network (CSR-Net) to address these issues. Specifically, we first design an adaptive CutmullRom splines fitting scheme to predict coarse forged regions. Subsequently, we develop a novel rescoring mechanism that filters out samples with no response in both the classification and instance branches to reduce false positives. Besides, a learnable texture extraction module decouples horizontal and vertical forgery features, extracting more robust contour representations to further refine boundaries and suppress false detections. Compared to segmentation-based methods our method is simple but effective due to the unnecessity of post-processing. Extensive experiments conducted on several challenging benchmarks demonstrate that our method outperforms state-of-the-art methods qualitatively and quantitatively. Particularly, CSR-Net achieves optimal performance on three real-world datasets, indicating the applicability of our method to real scenarios such as social media and multi-tampered regions.

关键词： Splines (mathematics) Forgery Location awareness Electronic mail image segmentation Accuracy Shape Process control Interpolation Feature extraction image forgery detection and localization catmullrom splines regression methods

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Multi-Frame Super-Resolution Phase Retrieval Based on Consensus Equalization

Multi-Frame Super-Resolution Phase Retrieval Based on Consen...

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2024 International Conference on New Trends in computational intelligence, NTCI 2024

作者： Jiang, Aiwei Guo, Bing College of Mathematics Statistics JiShou University Hunan China

ISBN: (纸本)9798331517021

Super-resolution phase retrieval refers to the reconstruction of high-resolution images from amplitude information, which plays a crucial role in the field of image processing. This paper proposes a multi-frame super-resolution phase retrieval algorithm based on consensus equalization, which balances the data fidelity term of the super-resolution phase retrieval with the TV denoising term. The TV denoising algorithm is combined with the traditional projection algorithm. This paper also tests the proposed algorithm in simulation data and evaluates the algorithm for the case at different Gaussian noise levels and different frames, where it is proved that the consensus equalization outperforms other algorithms with strong image reconstruction capability. © 2024 ieee.

关键词： image reconstruction

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Resource Allocation for Task-Oriented Generative Artificial intelligence in Internet of Things

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ieee INTERNET OF THINGS JOURNAL 2025年第10期12卷 13233-13247页

作者： Feng, Jie Huang, Xinqi Liu, Lei Yang, Mengmeng Pei, Qingqi Shee, Yu Gang Xidian Univ Sch Telecommun Engn Xidian 710071 Shaanxi Peoples R China CSIRO Data61 Melbourne Vic 3168 Australia Univ Tunku Abdul Rahman Ctr Elect Syst Design Kajang 43000 Malaysia

The implementation of the Internet of Things (IoT) technology has the potential to unleash the capabilities of generative artificial intelligence (GAI). However, integrating GAI with IoT introduces a significant challenge in managing the limited resources of edge networks. In this article, we propose a resource optimization framework for GAI in IoT systems to address this issue, leveraging a heterogeneous computing framework. We focus on the system utility maximization problem, which jointly optimizes transmit power, heterogeneous computing allocation, CPU-cycle frequency, GPU-cycle frequency, and task scheduling under the latency constraint. The optimal CPU-cycle frequency, GPU-cycle frequency, and computing allocation are obtained by employing data parallelism analysis. In particular, we develop a hierarchical soft actor-critic with an intrinsic curiosity (HSAC-IC) algorithm to determine the task scheduling strategy. The HSAC-IC algorithm utilizes a hierarchical strategy structure and an intrinsic curiosity module (ICM) to improve learning efficiency and performance, particularly in environments characterized by sparse rewards, high-dimensional action spaces, and complex tasks. Our simulations benchmark the HSAC-IC algorithm against two existing deep reinforcement learning (DRL) algorithms and three reference schemes. The results illustrate that our scheme significantly outperforms these alternatives, ensuring AIGC user service requirements, while minimizing service generation costs, and optimizing resource allocation by configuring the image quality strategy on edge servers.

关键词： Resource management Internet of Things computational modeling Servers Radio spectrum management Optimization image edge detection Graphics processing units Generative AI Energy consumption Generative artificial intelligence (GAI) hierarchical soft actor-critic with an intrinsic curiosity (HSAC-IC) Internet of Things (IoT) resource optimization

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image Super-Resolution With Content-Aware Feature processing

IEEE Transactions on Artificial Intelligence

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ieee Transactions on Artificial intelligence 2024年第1期5卷 179-191页

作者： Mehta, Nancy Murala, Subrahmanyam Indian Institute of Technology Ropar Department of Electrical Engineering Rupnagar140001 India

image super-resolution (SR) is currently a very active research topic with applications spanning from computer vision to videos and graphic industries. The top performers in SR field usually employ deep or wide convolutional neural networks (CNNs) to restore the lost textures from low-resolution images. However, most of these methods adopt pixel shuffle and deconvolution as their upsampling techniques and often generate conspicuous artifacts in the reconstructed image. In addition, the ongoing trend of directly portraying the degraded low-resolution image to a high-resolution image via complex deep CNNs improves the reconstruction performance, but at the cost of high computational complexity. In this article, we propose a multilevel bicubic upsampler network for reconstructing high-quality SR image with a restricted number of parameters. A novel content-aware feature difference (CAFD) block is presented to reform the network by focusing on contextual information. The proposed CAFD block consists of four multilevel attention blocks for a better extraction of low-level features at different scales. Furthermore, we design an innovative upsampling layer that consistently outperforms the traditional upsampling methods. These components collaboratively endow our proposed network with a great performance boost, helping it achieve state-of-the-art accuracy on five synthetic benchmark datasets, both qualitatively and quantitatively. In addition, a detailed ablation study has been accomplished to scrutinize the improvements obtained by different modules in the proposed method. © 2020 ieee.

关键词： image reconstruction

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Unsupervised Hyperspectral image Classification: Spatial and Spectral Feature Fusion with Masked Autoencoders 20

Unsupervised Hyperspectral Image Classification: Spatial and...

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20th CSI International symposium on Artificial intelligence and Signal processing, AISP 2024

作者： Mirsharji, Seyed Ali Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

In this study, we present an innovative unsupervised hyperspectral image classification method using a dual-branch architecture that merges spatial and spectral feature extraction. Our unique approach employs masked autoencoders, significantly outperforming traditional methods with an impressive overall accuracy of 97.1%. The paper details the model's performance evaluation, offers visual insights into its classification capabilities, and compares it with existing techniques, demonstrating its effectiveness and potential for advancing remote sensing applications. © 2024 ieee.

关键词： image classification

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MPCT: Multiscale Point Cloud Transformer With a Residual Network

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ieee TRANSACTIONS ON MULTIMEDIA 2024年 26卷 3505-3516页

作者： Wu, Yue Liu, Jiaming Gong, Maoguo Liu, Zhixiao Miao, Qiguang Ma, Wenping Xidian Univ Sch Comp Sci & Technol Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China Harbin Engn Univ Yantai Res Inst Yantai 264006 Peoples R China Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

The self-attention (SA) network revisits the essence of data and has achieved remarkable results in text processing and image analysis. SA is conceptualized as a set operator that is insensitive to the order and number of data, making it suitable for point sets embedded in 3D space. However, working with point clouds still poses challenges. To tackle the issue of exponential growth in complexity and singularity induced by the original SA network without position encoding, we modify the attention mechanism by incorporating position encoding to make it linear, thus reducing its computational cost and memory usage and making it more feasible for point clouds. This article presents a new framework called multiscale point cloud transformer (MPCT), which improves upon prior methods in cross-domain applications. The utilization of multiple embeddings enables the complete capture of the remote and local contextual connections within point clouds, as determined by our proposed attention mechanism. Additionally, we use a residual network to facilitate the fusion of multiscale features, allowing MPCT to better comprehend the representations of point clouds at each stage of attention. Experiments conducted on several datasets demonstrate that MPCT outperforms the existing methods, such as achieving accuracies of 94.2% and 84.9% in classification tasks implemented on ModelNet40 and ScanObjectNN, respectively.

关键词： Geometric and semantic features multiscale generation point cloud transformer residual network

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