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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Lianxin Xie Csbingbing Zheng Wen Xue Le Jiang Cheng Liu Si Wu Hau San Wong School of Computer Science and Engineering South China University of Technology Department of Computer Science Shantou University Peng Cheng Laboratory PAZHOU LAB Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Blind face restoration focuses on restoring high-fidelity details from images subjected to complex and unknown degradations, while preserving identity information. In this paper, we present a Prior-based Latent Transformation approach (PLTrans), which is specifically designed to learn a degradation-unaware representation, thereby allowing the restoration network to effectively generalize to real-world degradation. Toward this end, PLTrans learns a degradation-unaware query via a latent diffusion-based regularization module. Furthermore, conditioned on the features of a degraded face image, a latent dictionary that captures the priors of HQ face images is leveraged to refine the features by mapping the top-d nearest elements. The refined version will be used to build key and value for the cross-attention computation, which is tailored to each degraded image and exhibits reduced sensitivity to different degradation factors. Conditioned on the resulting representation, we train a decoding network that synthesizes face images with authentic details and identity preservation. Through extensive experiments, we verify the effectiveness of the design elements and demonstrate the generalization ability of our proposed approach for both synthetic and unknown degradations. We finally demonstrate the applicability of PLTrans in other vision tasks.

关键词： Degradation Dictionaries Sensitivity Face recognition Transforms Transformers Vectors

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Multi-Step Hindsight Experience Replay with Bias Reduction for Efficient Multi-Goal Reinforcement Learning

Multi-Step Hindsight Experience Replay with Bias Reduction f...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： Yang, Yu Yang, Rui Lyu, Jiafei Yan, Jiangpeng Luo, Feng Luo, Dijun Li, Xiu Li, Lanqing Tsinghua University Shenzhen International Graduate School Shenzhen China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Ai Lab Tencent Inc. Shenzhen China

ISBN: (纸本)9798350301113

Multi-goal reinforcement learning has emerged as a powerful approach for planning and robot manipulation tasks, but it faces challenges such as sparse rewards and sample inefficiency. Hindsight Experience Replay (HER) has been proposed as a solution to these challenges by relabeling goals, but it still requires a large number of samples and significant computation. To address these issues, we propose Multi-step Hindsight Experience Replay (MHER), which incorporates multi-step relabeling to improve sample efficiency. Despite the advantages of n -step relabeling, we theoretically and experimentally prove the off-policy n -step bias introduced by n -step relabeling may lead to poor performance in many environments. To address this issue, two bias-reduced MHER algorithms, MHER ( λ ) and Model-based MHER (MMHER) are presented. MHER ( λ ) exploits the λ return while MMHER benefits from model-based value expansions. Experimental results on numerous multi-goal robotic tasks show that our solutions can successfully alleviate the off-policy n -step bias and achieve significantly higher sample efficiency than previous multi-goal RL baselines with little additional computation beyond HER. © 2023 IEEE.

关键词： Reinforcement learning

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Retracted: Using Markov predictions for handling and allocating task to virtual machines in clouds data centres

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IET Communications 2023年第13期17卷 1562-1576页

作者： Zavieh, Hadi Javadpour, Amir Li, Yuan Ja'fari, Forough Sangaiah, Arun Kumar Zhang, Weizhe Nasseri, Seyed Hadi School of Economics and Statistics Guangzhou University Guangzhou University Guangzhou China Harbin Institute of Technology Harbin China ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto Portugal International Graduate Institute of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Mathematics University of Mazandaran Babolsar Iran

The above article from IET Communications, published online on 16 December 2022 in Wiley Online Library (***), has been retracted by agreement between the Interim Editor-in-Chief, Jian Ren, the Institution of Engineering and technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. © 2022 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Virtual machine

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Convolutional Neural Network Based Intelligent Handwritten Document Recognition

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computers, Materials & Continua 2022年第3期70卷 4563-4581页

作者： Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Bangi 43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Department of Computer Science GC UniversityLahorePakistan Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

This paper presents a handwritten document recognition system based on the convolutional neural network *** today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired *** technology is also helpful for the automatic data entry *** the proposed systemprepared a dataset of English language handwritten character *** proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten *** this research,multiple experiments get very worthy recognition *** proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural *** this processing,the input document is segmented using line,word and character *** proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their *** recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given *** proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

关键词： Convolutional neural network segmentation skew cursive characters recognition

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A Global Best-guided Firefly Algorithm for Engineering Problems

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Journal of Bionic Engineering 2023年第5期20卷 2359-2388页

作者： Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical Engineering Shiraz University of TechnologyShiraz***Iran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran***Iran Department of Computer Engineering Sari BranchIslamic Azad UniversitySari***Iran Department of Electrical Engineering Urmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan School of Computer Sciences Universiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation Center Obuda University1034BudapestHungary School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Applied science research center Applied science private universityAmman11931Jordan

The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.

关键词： Firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization Engineering design

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SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation

arXiv

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arXiv 2024年

作者： Wang, Chengjie Jiang, Xi Gao, Bin-Bin Gan, Zhenye Liu, Yong Zheng, Feng Ma, Lizhuang Department of Computer Science and Engineering Shanghai Jiao Tong University China Youtu Lab Tencent China Department of Computer Science and Engineering Southern University of Science and Technology China

Although mainstream unsupervised anomaly detection (AD) (including image-level classification and pixel-level segmentation) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper is the first to consider fully unsupervised industrial anomaly detection (i.e., unsupervised AD with noisy data). To solve this problem, we proposed memory-based unsupervised AD methods, SoftPatch and SoftPatch+, which efficiently denoise the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset, and SoftPatch+ has more robust performance which is particularly useful in real-world industrial inspection scenarios with high levels of noise (from 10% to 40%). Comprehensive experiments conducted in diverse noise scenarios demonstrate that both SoftPatch and SoftPatch+ outperform the state-of-the-art AD methods on the MVTecAD, ViSA, and BTAD benchmarks. Furthermore, the performance of SoftPatch and SoftPatch+ is comparable to that of the noise-free methods in conventional unsupervised AD setting. The code of the proposed methods can be found at https://***/TencentYoutuResearch/AnomalyDetection-SoftPatch. © 2024, CC BY.

关键词： Image segmentation

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Text-Conditional Attribute Alignment Across Latent Spaces for 3D Controllable Face Image Synthesis

Text-Conditional Attribute Alignment Across Latent Spaces fo...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Feifan Xu Rui Li Si Wu Yong Xu Hau San Wong School of Computer Science and Engineering South China University of Technology Department of Computer Science Shantou University Peng Cheng Laboratory PAZHOU LAB Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

With the advent of generative models and vision-language pre-training, significant improvement has been made in text-driven face manipulation. The text embedding can be used as target supervision for expression control. However, it is non-trivial to associate with its 3D attributes, i.e., pose and illumination. To address these issues, we propose a Text-conditional Attribute aLignment approach for 3D controllable face image synthesis, and our model is referred to as TcALign. Specifically, since the 3D rendered image can be precisely controlled with the 3D face representation, we first propose a Text-conditional 3D Editor to produce the target face representation to realize text-driven manipulation in the 3D space. An attribute embedding space spanned by the target-related attributes embeddings is also introduced to infer the disentangled task-specific direction. Next, we train a cross-modal latent mapping network conditioned on the derived difference of 3D representation to infer a correct vector in the latent space of Style-GAN. This correction vector learning design can accurately transfer the attribute manipulation on 3D images to 2D images. We show that the proposed method delivers more precise text-driven multi-attribute manipulation for 3D controllable face image synthesis. Extensive qualitative and quantitative experiments verify the effectiveness and superiority of our method over the other competing methods.

关键词： Solid modeling computer vision Three-dimensional displays Image synthesis Face recognition Computational modeling Lighting

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Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization

arXiv

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arXiv 2025年

作者： Ma, Youpeng Chen, Tao Li, Ke School of Computer Science and Engineering University of Electronic Science and Technology of China China IDEAS Lab School of Computer Science University of Birmingham United Kingdom Department of Computer Science University of Exeter United Kingdom

As software systems become more complex and configurable, more performance problems tend to arise from the configuration designs. This has caused some configuration options to unexpectedly degrade performance which deviates from their original expectations designed by the developers. Such discrepancies, namely configuration performance bugs (CPBugs), are devastating and can be deeply hidden in the source code. Yet, efficiently testing CPBugs is difficult, not only due to the test oracle is hard to set, but also because the configuration measurement is expensive and there are simply too many possible configurations to test. As such, existing testing tools suffer from lengthy runtime or have been ineffective in detecting CPBugs when the budget is limited, compounded by inaccurate test oracle. In this paper, we seek to achieve significantly faster CPBug testing by neurally prioritizing the testing at both the configuration option and value range levels with automated oracle estimation. Our proposed tool, dubbed NDP, is a general framework that works with different heuristic generators. The idea is to leverage two neural language models: one to estimate the CPBug types that serve as the oracle while, more vitally, the other to infer the probabilities of an option being CPBug-related, based on which the options and the value ranges to be searched can be prioritized. Experiments on several widely-used systems of different versions reveal that NDP can, in general, better predict CPBug type in 87% cases and find more CPBugs with up to 88.88× testing efficiency speedup over the state-of-the-art tools. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Trans-Mamba: The Cross-network of Transformer and Mamba for Traffic Flow Prediction 10

Trans-Mamba: The Cross-network of Transformer and Mamba for ...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Qian Jia, Haonan Ye, Junchen Feng, Jinyan Lan, Fayang Du, Bowen Huang, Runhe Beihang University Ccse Lab Beijing China Beihang University School of Transportation Science and Engineering Beijing China Guizhou Qiantong Zhilian Technology Co. Ltd Guizhou China Beihang University and Zhongguancun Laboratory Transportation Science and Engineering Beijing China Hosei University Faculty of Computer and Information Sciences Department of Computer Science Metropolis Tokyo Japan

ISBN: (纸本)9798331520861

Traffic flow prediction is a crucial aspect of intelligent transportation systems. It holds significant importance in optimizing traffic management and enhancing road safety. However, the task of traffic flow forecasting remains a significant challenge due to its inherent complexity, dynamic nature, and the presence of long-range dependencies. Existing graph neural network methods exhibit two primary limitations. Firstly, when static graphs are employed to capture spatial dependencies, they fail to account for the dynamics in these dependencies. Secondly, these methods struggle to effectively capture long-range spatial dependencies. Furthermore, attention-based methods, while potentially addressing some of these issues, require substantial computational and memory resources, leading to inefficiencies. We design a cross-network model that combines Transformer and Mamba architectures for traffic flow prediction, which efficiently captures dynamic long-range spatio-temporal dependencies. The model architecture is composed of three main components: a learnable spatio-temporal embedding layer that adaptively captures the unique features of different nodes and time;stacked spatio-temporal blocks, which include two temporal blocks and one spatial block arranged between them to effectively capture spatio-temporal dependencies;and a fully connected output layer that generates the final predictions. Experiments on three public real-world datasets demonstrate the effectiveness of TransMamba. Specifically, the model improves the mean absolute error (MAE) and root mean square error (RMSE) by 2.59% and 4.14%, respectively, on the PeMS08 dataset. Additionally, ablation studies confirm the effectiveness and efficiency of each component of the model. © 2024 IEEE.

关键词： Graph neural networks

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