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Deep Unrolled Weighted Graph Laplacian Regularization for Depth Completion Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-25 Jin Zeng, Qingpeng Zhu, Tongxuan Tian, Wenxiu Sun, Lin Zhang, Shengjie Zhao
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MAS-CL: An End-to-end Multi-atlas Supervised Contrastive Learning Framework for Brain ROI Segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-25 Liang Sun, Yanling Fu, Junyong Zhao, Wei Shao, Qi Zhu, Daoqiang Zhang
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Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-25 Yang Yang, Wenming Xu, Anguo Gao, Qingshan Yang, Yuqing Gao
Considering the weak noise resistance and low identification efficiency of traditional bridge damage identification methods, a data‐driven approach based on synchronous statistical moment theory and vehicle–bridge interaction vibration theory is proposed. This method involves two main steps. First, a two‐axle test vehicle is used to collect acceleration response signals synchronously from adjacent
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SRConvNet: A Transformer-Style ConvNet for Lightweight Image Super-Resolution Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-24 Feng Li, Runmin Cong, Jingjing Wu, Huihui Bai, Meng Wang, Yao Zhao
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Multispectral Image Stitching via Global-Aware Quadrature Pyramid Regression IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-24 Zhiying Jiang, Zengxi Zhang, Jinyuan Liu, Xin Fan, Risheng Liu
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Multicategory fire damage detection of post‐fire reinforced concrete structural components Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-24 Pengfei Wang, Caiwei Liu, Xinyu Wang, Libin Tian, Jijun Miao, Yanchun Liu
This paper introduces an enhanced you only look once (YOLO) v5s‐D network customized for detecting various categories of damage to post‐fire reinforced concrete (RC) components. These damage types encompass surface soot, cracks, concrete spalling, and rebar exposure. A dataset containing 1536 images depicting damaged RC components was compiled. By integrating ShuffleNet, adaptive attention mechanisms
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Telescopic broad Bayesian learning for big data stream Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-24 Ka‐Veng Yuen, Sin‐Chi Kuok
In this paper, a novel telescopic broad Bayesian learning (TBBL) is proposed for sequential learning. Conventional broad learning suffers from the singularity problem induced by the complexity explosion as data are accumulated. The proposed TBBL successfully overcomes the challenging issue and is feasible for sequential learning with big data streams. The learning network of TBBL is reconfigurable
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Continuous Spatial-Spectral Reconstruction via Implicit Neural Representation Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-22 Ruikang Xu, Mingde Yao, Chang Chen, Lizhi Wang, Zhiwei Xiong
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Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-23 Wenrui Li, Penghong Wang, Ruiqin Xiong, Xiaopeng Fan
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Spiking Transfer Learning from RGB Image to Neuromorphic Event Stream IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-23 Qiugang Zhan, Guisong Liu, Xiurui Xie, Ran Tao, Malu Zhang, Huajin Tang
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A neural network‐based automated methodology to identify the crack causes in masonry structures Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-23 A. Iannuzzo, V. Musone, E. Ruocco
Most masonry constructions exhibit significant crack patterns caused by differential foundation settlements. While modern numerical methods effectively address forward displacement‐based problems, identifying the settlement causing a specific crack pattern remains an unsolved yet crucial challenge. For the first time, this research solves this highly non‐linear back‐engineering problem by proposing
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Cover Image, Volume 39, Issue 15 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-23
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Cover Image, Volume 39, Issue 15 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-23
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FADE: A Task-Agnostic Upsampling Operator for Encoder–Decoder Architectures Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-22 Hao Lu, Wenze Liu, Hongtao Fu, Zhiguo Cao
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Learning Combinatorial Prompts for Universal Controllable Image Captioning Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-22 Zhen Wang, Jun Xiao, Yueting Zhuang, Fei Gao, Jian Shao, Long Chen
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Incremental Model Enhancement via Memory-based Contrastive Learning Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-20 Shiyu Xuan, Ming Yang, Shiliang Zhang
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Learning Dynamic Batch-Graph Representation for Deep Representation Learning Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-19 Xixi Wang, Bo Jiang, Xiao Wang, Bin Luo
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A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-18 Jian Liang, Ran He, Tieniu Tan
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Exploring Multi-modal Spatial-Temporal Contexts for High-performance RGB-T Tracking IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-19 Tianlu Zhang, Qiang Jiao, Qiang Zhang, Jungong Han
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Towards Unsupervised Domain Adaptation via Domain-Transformer Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-16 Chuan-Xian Ren, Yiming Zhai, You-Wei Luo, Hong Yan
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Towards Training-Free Open-World Segmentation via Image Prompt Foundation Models Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-16 Lv Tang, Peng-Tao Jiang, Haoke Xiao, Bo Li
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Vision Transformers: From Semantic Segmentation to Dense Prediction Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-16 Li Zhang, Jiachen Lu, Sixiao Zheng, Xinxuan Zhao, Xiatian Zhu, Yanwei Fu, Tao Xiang, Jianfeng Feng, Philip H. S. Torr
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UPR-Net: A Unified Pyramid Recurrent Network for Video Frame Interpolation Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-17 Xin Jin, Longhai Wu, Jie Chen, Youxin Chen, Jayoon Koo, Cheul-Hee Hahm, Zhao-Min Chen
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Robust Image Restoration with an Adaptive Huber Function Based Fidelity Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-15 Lingfei Song, Hua Huang
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Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-15 Kun Fang, Qinghua Tao, Xiaolin Huang, Jie Yang
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Cyclic Refiner: Object-Aware Temporal Representation Learning for Multi-view 3D Detection and Tracking Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-16 Mingzhe Guo, Zhipeng Zhang, Liping Jing, Yuan He, Ke Wang, Heng Fan
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An efficient static solver for the lattice discrete particle model Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-16 Dongge Jia, John C. Brigham, Alessandro Fascetti
The lattice discrete particle model (LDPM) has been proven to be one of the most appealing computational tools to simulate fracture in quasi‐brittle materials. Despite tremendous advancements in the definition and implementation of the method, solution strategies are still limited to dynamic algorithms, resulting in prohibitive computational costs and challenges related to solution accuracy for quasi‐static
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Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-15 Tianshui Chen, Tao Pu, Lingbo Liu, Yukai Shi, Zhijing Yang, Liang Lin
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Unsupervised Domain Adaptation via Domain-Adaptive Diffusion IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-15 Duo Peng, Qiuhong Ke, ArulMurugan Ambikapathi, Yasin Yazici, Yinjie Lei, Jun Liu
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OTAMatch: Optimal Transport Assignment with PseudoNCE for Semi-supervised Learning IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-15 Jinjin Zhang, Junjie Liu, Debang Li, Qiuyu Huang, Jiaxin Chen, Di Huang
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Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-15 Haolin Pan, Yong Guo, Mianjie Yu, Jian Chen
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HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-15 Guoan Xu, Wenjing Jia, Tao Wu, Ligeng Chen, Guangwei Gao
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Remote Sensing Change Detection With Bitemporal and Differential Feature Interactive Perception IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-15 Hao Chang, Peijin Wang, Wenhui Diao, Guangluan Xu, Xian Sun
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Procedure-Aware Action Quality Assessment: Datasets and Performance Evaluation Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-14 Jinglin Xu, Yongming Rao, Jie Zhou, Jiwen Lu
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Multi-modal Prototypes for Open-World Semantic Segmentation Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-12 Yuhuan Yang, Chaofan Ma, Chen Ju, Fei Zhang, Jiangchao Yao, Ya Zhang, Yanfeng Wang
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Fast Global Image Smoothing via Quasi Weighted Least Squares Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-13 Wei Liu, Pingping Zhang, Hongxing Qin, Xiaolin Huang, Jie Yang, Michael Ng
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Exploiting Diffusion Prior for Real-World Image Super-Resolution Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-11 Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin C. K. Chan, Chen Change Loy
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CD-iNet: Deep Invertible Network for Perceptual Image Color Difference Measurement Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-12 Zhihua Wang, Keshuo Xu, Keyan Ding, Qiuping Jiang, Yifan Zuo, Zhangkai Ni, Yuming Fang
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End-to-End Video Text Spotting with Transformer Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-12 Weijia Wu, Yuanqiang Cai, Chunhua Shen, Debing Zhang, Ying Fu, Hong Zhou, Ping Luo
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Learning Feature Restoration Transformer for Robust Dehazing Visual Object Tracking Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-12 Tianyang Xu, Yifan Pan, Zhenhua Feng, Xuefeng Zhu, Chunyang Cheng, Xiao-Jun Wu, Josef Kittler
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A non‐contact identification method of overweight vehicles based on computer vision and deep learning Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-12 Daoheng Li, Meiyu Liu, Lu Yang, Han Wei, Jie Guo
The phenomenon of overweight vehicles severely threatens traffic safety and the service life of transportation infrastructure. Rapid and effective identification of overweight vehicles is of significant importance for maintaining the healthy operation of highways and bridges and ensuring the safety of people's lives and property. With the problems of high cost and low efficiency, the traditional vehicle
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A fair and scalable watermarking scheme for the digital content trading industry Comput. Ind. (IF 8.2) Pub Date : 2024-07-12 Xiangli Xiao, Moting Su, Jiajia Jiang, Yushu Zhang, Zhongyun Hua, Zhihua Xia
The booming Internet economy and generative artificial intelligence have driven the rapid growth of the digital content trading industry, creating an urgent need for the fair protection of the rights of both buyers and sellers. To meet this need, a technique known as buyer–seller watermarking has emerged. Despite its existence, the majority of existing buyer–seller watermarking schemes adopt the owner-side
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Multi-source-free Domain Adaptive Object Detection Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-11 Sicheng Zhao, Huizai Yao, Chuang Lin, Yue Gao, Guiguang Ding
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Cayley Rotation Averaging: Multiple Camera Averaging Under the Cayley Framework IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-11 Qiulei Dong, Shuang Deng, Yuzhen Liu
Rotation averaging, which aims to calculate the absolute rotations of a set of cameras from a redundant set of their relative rotations, is an important and challenging topic arising in the study of structure from motion. A central problem in rotation averaging is how to alleviate the influence of noise and outliers. Addressing this problem, we investigate rotation averaging under the Cayley framework
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Scalable Deep Color Quantization: a Cluster Imitation Approach IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-11 Yunzhong Hou, Stephen Gould, Liang Zheng
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Multiresolution dynamic mode decomposition approach for wind pressure analysis and reconstruction around buildings Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-11 Reda Snaiki, Seyedeh Fatemeh Mirfakhar
Accurate wind pressure analysis on high‐rise buildings is critical for wind load prediction. However, traditional methods struggle with the inherent complexity and multiscale nature of these data. Furthermore, the high cost and practical limitations of deploying extensive sensor networks restrict the data collection capabilities. This study addresses these limitations by introducing a novel framework
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Physics‐informed neural operator solver and super‐resolution for solid mechanics Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-11 Chawit Kaewnuratchadasorn, Jiaji Wang, Chul‐Woo Kim
Physics‐Informed Neural Networks (PINNs) have solved numerous mechanics problems by training to minimize the loss functions of governing partial differential equations (PDEs). Despite successful development of PINNs in various systems, computational efficiency and fidelity prediction have remained profound challenges. To fill such gaps, this study proposed a Physics‐Informed Neural Operator Solver
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Learning Virtual View Selection for 3D Scene Semantic Segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-10 Tai-Jiang Mu, Ming-Yuan Shen, Yu-Kun Lai, Shi-Min Hu
2D-3D joint learning is essential and effective for fundamental 3D vision tasks, such as 3D semantic segmentation, due to the complementary information these two visual modalities contain. Most current 3D scene semantic segmentation methods process 2D images “as they are”, i.e., only real captured 2D images are used. However, such captured 2D images may be redundant, with abundant occlusion and/or
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Infproto-Powered Adaptive Classifier and Agnostic Feature Learning for Single Domain Generalization in Medical Images Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-08 Xiaoqing Guo, Jie Liu, Yixuan Yuan
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Deep learning‐based segmentation model for permeable concrete meso‐structures Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-09 De Chen, Yukun Li, Jiaxing Tao, Yuchen Li, Shilong Zhang, Xuehui Shan, Tingting Wang, Zhi Qiao, Rui Zhao, Xiaoqiang Fan, Zhongrong Zhou
The meso‐structure of pervious concrete significantly influences its overall performance. Accurately identifying the meso‐structure of pervious concrete is imperative for optimizing the design of pervious concrete, considering its mechanical properties and functionality. Therefore, to address the difficulty of recognizing the meso‐structures of pervious concrete, a method utilizing deep learning image
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Exploration and Exploitation of Unlabeled Data for Open-Set Semi-supervised Learning Int. J. Comput. Vis. (IF 11.6) Pub Date : 2024-07-08 Ganlong Zhao, Guanbin Li, Yipeng Qin, Jinjin Zhang, Zhenhua Chai, Xiaolin Wei, Liang Lin, Yizhou Yu
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Optimizing green splits in high‐dimensional traffic signal control with trust region Bayesian optimization Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-08 Yunhai Gong, Shaopeng Zhong, Shengchuan Zhao, Feng Xiao, Wenwen Wang, Yu Jiang
Centralized traffic signal control has long been a challenging, high‐dimensional optimization problem. This study establishes a simulation‐based optimization framework and develops a novel optimization algorithm based on trust region Bayesian optimization (TuRBO), which can efficiently obtain an approximate optimal solution to the high‐dimensional traffic signal control problem. Local Gaussian process
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Corrigendum to “Deep spatial‐temporal embedding for vehicle trajectory validation and refinement” Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-08
Zhang, T. T., Jin, P. J., Piccoli, B., & Sartipi, M. (2024). Deep spatial-temporal embedding for vehicle trajectory validation and refinement. Computer-Aided Civil and Infrastructure Engineering, 39, 1597−1615. https://doi.org/10.1111/mice.13160 In the “Methodology” section, Equation (2) “” was incorrect. The correct equation should have been written as “” In the “Methodology” section, Equation (3)
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Automatic generation of architecture drawings from point clouds Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-07 Fengyu Zhang, Qingzhao Kong, Cheng Yuan, Peizhen Li
Traditional methods for producing architectural drawings require extensive manual labor. This paper proposes an automated method for generating a comprehensive set of three‐view drawings, including the standardized labeling of doors and annotation of dimensions and areas. The output drawings are software‐readable and editable, and the method is applicable to intricate structures with non‐orthogonal
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Ego‐planning‐guided multi‐graph convolutional network for heterogeneous agent trajectory prediction Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-07 Zihao Sheng, Zilin Huang, Sikai Chen
Accurate prediction of the future trajectories of traffic agents is a critical aspect of autonomous vehicle navigation. However, most existing approaches focus on predicting trajectories from a static roadside perspective, ignoring the influence of autonomous vehicles’ future plans on neighboring traffic agents. To address this challenge, this paper introduces EPG‐MGCN, an ego‐planning‐guided multi‐graph
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CFD-ML: Stream-based active learning of computational fluid dynamics simulations for efficient product design Comput. Ind. (IF 8.2) Pub Date : 2024-07-05 Youngjae Bae, Kyunghye Nam, Seokho Kang
Computational fluid dynamics (CFD) has been extensively used as a simulation tool for product development in various industrial fields. Engineers sequentially query the CFD simulator to evaluate their design instances, during which they improve the new designs based on previous evaluations. The high cost of performing CFD simulations for numerous design instances is a practical challenge. To reduce
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Enhancing Low-Light Light Field Images With a Deep Compensation Unfolding Network IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-04 Xianqiang Lyu, Junhui Hou
This paper presents a novel and interpretable end-to-end learning framework, called the deep compensation unfolding network (DCUNet), for restoring light field (LF) images captured under low-light conditions. DCUNet is designed with a multi-stage architecture that mimics the optimization process of solving an inverse imaging problem in a data-driven fashion. The framework uses the intermediate enhanced
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Spectral Embedding Fusion for Incomplete Multiview Clustering IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-04 Jie Chen, Yingke Chen, Zhu Wang, Haixian Zhang, Xi Peng
Incomplete multiview clustering (IMVC) aims to reveal the underlying structure of incomplete multiview data by partitioning data samples into clusters. Several graph-based methods exhibit a strong ability to explore high-order information among multiple views using low-rank tensor learning. However, spectral embedding fusion of multiple views is ignored in low-rank tensor learning. In addition, addressing
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One-Shot Any-Scene Crowd Counting With Local-to-Global Guidance IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-07-04 Jiwei Chen, Zengfu Wang