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Towards Boosting Out-of-Distribution Detection from a Spatial Feature Importance Perspective Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-02-05 Yao Zhu, Xiu Yan, Chuanlong Xie
In ensuring the reliable and secure operation of models, Out-of-Distribution (OOD) detection has gained widespread attention in recent years. Researchers have proposed various promising detection criteria to construct the rejection region of the model, treating samples falling into this region as out-of-distribution. However, these detection criteria are computed using all dense features of the model
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Automated corrosion surface quantification in steel transmission towers using UAV photogrammetry and deep convolutional neural networks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-02-05 Pierclaudio Savino, Fabio Graglia, Gabriele Scozza, Vincenzo Di Pietra
Corrosion in steel transmission towers poses a challenge to structural integrity and safety, requiring efficient detection methods. Traditional visual inspections are unsustainable due to the complexity and volume of structures. Their manual, qualitative, and subjective nature often leads to inconsistencies in maintenance planning. This study proposes a deep learning‐based approach for semantic segmentation
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Issue Information Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-02-05
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An immersive spatially consistent multi-modal augmented virtuality human-machine interface for telerobotic systems Comput. Ind. (IF 8.2) Pub Date : 2025-02-05 Rebecca Schwenk, Shana Smith
This study presents an immersive augmented virtuality (AV)-based human-machine interface (HMI) designed to enhance telepresence and operator performance in telerobotic systems. Traditional telerobotic systems often face limitations such as 2D representations of 3D environments, restricted fields of view, and reduced depth perception, all of which hinder operator effectiveness. Although extended reality
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Measure2Shape: A novel footwear customisation framework utilising 3D shape estimation from anthropometric measurements with an orthosis case study Comput. Ind. (IF 8.2) Pub Date : 2025-02-05 Zhaohua Zhu, Wenxuan Ji, Yadie Yang, Sio-Kei Im, Jie Zhang
To address the limitations of relying on expensive 3D scanners for obtaining foot data in footwear customisation, this paper introduces a novel framework, Measure2Shape, which estimates 3D foot shapes using anthropometric measurement data. To achieve this, we established a large-scale 3D foot dataset with measurement data and created statistical shape models (SSMs) to represent the range of foot variations
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Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-02-04 Tianshui Chen, Jianman Lin, Zhijing Yang, Chumei Qing, Yukai Shi, Liang Lin
Speech-preserving facial expression manipulation (SPFEM) aims to modify a talking head to display a specific reference emotion while preserving the mouth animation of source spoken contents. Thus, emotion and content information existing in reference and source inputs can provide direct and accurate supervision signals for SPFEM models. However, the intrinsic intertwining of these elements during the
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Rethinking Copy-Paste for Consistency Learning in Medical Image Segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-02-04 Senlong Huang, Yongxin Ge, Dongfang Liu, Mingjian Hong, Junhan Zhao, Alexander C. Loui
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Joint Spatial and Frequency Domain Learning for Lightweight Spectral Image Demosaicing IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-02-04 Fangfang Wu, Tao Huang, Junwei Xu, Xun Cao, Weisheng Dong, Le Dong, Guangming Shi
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VSR-Net: Vessel-like Structure Rehabilitation Network with Graph Clustering IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-02-04 Haili Ye, Xiaoqing Zhang, Yan Hu, Huazhu Fu, Jiang Liu
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Decouple and Couple: Exploiting Prior Knowledge for Visible Video Watermark Removal IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-02-04 Junye Chen, Chaowei Fang, Jichang Li, Yicheng Leng, Guanbin Li
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Implicit-explicit Integrated Representations for Multi-view Video Compression IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-02-04 Chen Zhu, Guo Lu, Bing He, Rong Xie, Li Song
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All Roads Lead to Rome: Achieving 3D Object Encryption through 2D Image Encryption Methods IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-02-04 Ruoyu Zhao, Yushu Zhang, Rushi Lan, Shuang Yi, Zhongyun Hua, Jian Weng
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The expressway network design problem for multiple urban subregions based on the macroscopic fundamental diagram Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-02-04 Yunran Di, Weihua Zhang, Haotian Shi, Heng Ding, Jinbiao Huo, Bin Ran
With the advancement of urbanization, cities are constructing expressways to meet complex travel demands. However, traditional link‐based road network design methods face challenges in addressing large‐scale expressway network design problems. This study proposes an expressway network design method tailored for multi‐subregion road networks. The method employs the macroscopic fundamental diagram to
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Rethinking Feature Reconstruction via Category Prototype in Semantic Segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-02-03 Quan Tang, Chuanjian Liu, Fagui Liu, Jun Jiang, Bowen Zhang, C. L. Philip Chen, Kai Han, Yunhe Wang
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Evaluating unsignalized crosswalk safety in the age of autonomous vehicles Comput. Ind. (IF 8.2) Pub Date : 2025-02-03 Andrea Avignone, Marco Bassani, Beatrice Borgogno, Brunella Caroleo, Silvia Chiusano, Federico Princiotto
As autonomous vehicles are poised to enter public roadways, a major concern is their interaction with pedestrians. It requires attention and ability for pedestrians to interact correctly and for autonomous vehicles to detect pedestrians hence avoiding collisions. We propose a complete pipeline to collect, process and elaborate video data to quantitatively assess the possible occurrence of conflicts
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TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-31 Jiazheng Xing, Chao Xu, Yijie Qian, Yang Liu, Guang Dai, Baigui Sun, Yong Liu, Jingdong Wang
Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. However, the clothing identity uncontrollability and training inefficiency of existing diffusion-based methods, which struggle to maintain the identity even with full parameter training, are significant limitations that hinder the widespread
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DiffuVolume: Diffusion Model for Volume based Stereo Matching Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-02-01 Dian Zheng, Xiao-Ming Wu, Zuhao Liu, Jingke Meng, Wei-Shi Zheng
Stereo matching is a significant part in many computer vision tasks and driving-based applications. Recently cost volume-based methods have achieved great success benefiting from the rich geometry information in paired images. However, the redundancy of cost volume also interferes with the model training and limits the performance. To construct a more precise cost volume, we pioneeringly apply the
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Trustworthy Limited Data CT Reconstruction Using Progressive Artifact Image Learning IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-31 Jianjia Zhang, Zirong Li, Jiayi Pan, Shaoyu Wang, Weiwen Wu
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An integrated approach for enhanced early-phase space system design and optimization Comput. Ind. (IF 8.2) Pub Date : 2025-01-31 Yutong Zhang, Dong Ye, Cheng Wei, Zhaowei Sun
The integration of Model-Based Systems Engineering (MBSE) and Multidisciplinary Design Analysis and Optimization (MDAO) presents a powerful opportunity to enhance early-stage system design, particularly for complex space systems. However, the lack of efficient integration between these methods results in limitations such as unclear boundary between domain models, reduced automation, and challenges
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A Deep Transformer-based Fast CU Partition Approach for Inter-mode VVC IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-30 Tianyi Li, Mai Xu, Zheng Liu, Ying Chen, Kai Li
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Spiking Neural Networks with Adaptive Membrane Time Constant for Event-Based Tracking IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-30 Jiqing Zhang, Malu Zhang, Yuanchen Wang, Qianhui Liu, Baocai Yin, Haizhou Li, Xin Yang
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Autonomous construction framework for crane control with enhanced soft actor–critic algorithm and real‐time progress monitoring Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-30 Yifei Xiao, T. Y. Yang, Fan Xie
With the shortage of skilled labors, there is an increasing demand for automation in the construction industry. This study presents an autonomous construction framework for crane control with enhanced soft actor–critic (SAC‐E) algorithm and real‐time progress monitoring. SAC‐E is a novel reinforcement learning algorithm with superior learning speed and training stability for lifting path planning.
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Collaborative fault tolerance for cyber–physical systems: The detection stage Comput. Ind. (IF 8.2) Pub Date : 2025-01-30 Luis Piardi, André Schneider de Oliveira, Pedro Costa, Paulo Leitão
In the era of Industry 4.0, fault tolerance is essential for maintaining the robustness and resilience of industrial systems facing unforeseen or undesirable disturbances. Current methodologies for fault tolerance stages namely, detection, diagnosis, and recovery, do not correspond with the accelerated technological evolution pace over the past two decades. Driven by the advent of digital technologies
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A triple semantic-aware knowledge distillation network for industrial defect detection Comput. Ind. (IF 8.2) Pub Date : 2025-01-30 Zhitao Wen, Jinhai Liu, He Zhao, Qiannan Wang
Knowledge distillation (KD) is a powerful model compression technique that aims to transfer knowledge from heavy teacher networks to compact student networks via distillation. However, effectively transferring semantic knowledge in industrial settings poses significant challenges. On one hand, the appearance of defects (e.g., size and shape) may vary considerably due to the influence of the industrial
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Self-supervised Shutter Unrolling with Events Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-29 Mingyuan Lin, Yangguang Wang, Xiang Zhang, Boxin Shi, Wen Yang, Chu He, Gui-song Xia, Lei Yu
Continuous-time Global Shutter Video Recovery (CGVR) faces a substantial challenge in recovering undistorted high frame-rate Global Shutter (GS) videos from distorted Rolling Shutter (RS) images. This problem is severely ill-posed due to the absence of temporal dynamic information within RS intra-frame scanlines and inter-frame exposures, particularly when prior knowledge about camera/object motions
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USB-Net: Unfolding Split Bregman Method With Multi-Phase Feature Integration for Compressive Imaging IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-29 Zhen Guo, Hongping Gan
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Self-Supervised Monocular Depth Estimation with Dual-Path Encoders and Offset Field Interpolation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-29 Cheng Feng, Congxuan Zhang, Zhen Chen, Weiming Hu, Ke Lu, Liyue Ge
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Deep Label Propagation with Nuclear Norm Maximization for Visual Domain Adaptation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-29 Wei Wang, Hanyang Li, Cong Wang, Chao Huang, Zhengming Ding, Feiping Nie, Xiaochun Cao
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Self-assembled Generative Framework for Generalized Zero-shot Learning IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-29 Mengyu Gao, Qiulei Dong
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Hyperspectral Image Classification via Cascaded Spatial Cross-Attention Network IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-29 Bo Zhang, Yaxiong Chen, Shengwu Xiong, Xiaoqiang Lu
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A New Cross-Space Total Variation Regularization Model for Color Image Restoration with Quaternion Blur Operator IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-29 Zhigang Jia, Yuelian Xiang, Meixiang Zhao, Tingting Wu, Michael K. Ng
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Adaptive Bit Selection for Scalable Deep Hashing IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-29 Min Wang, Wengang Zhou, Xin Yao, Houqiang Li
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Temporal Fusion: Continuous-Time Light Field Video Factorization IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-29 Li-De Chen, Li-Qun Weng, Hao-Chien Cheng, An-Yu Cheng, Chao-Tsung Huang
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Image Synthesis Under Limited Data: A Survey and Taxonomy Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-27 Mengping Yang, Zhe Wang
Deep generative models, which target reproducing the data distribution to produce novel images, have made unprecedented advancements in recent years. However, one critical prerequisite for their tremendous success is the availability of a sufficient number of training samples, which requires massive computation resources. When trained on limited data, generative models tend to suffer from severe performance
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Dual-Space Video Person Re-identification Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-27 Jiaxu Leng, Changjiang Kuang, Shuang Li, Ji Gan, Haosheng Chen, Xinbo Gao
Video person re-identification (VReID) aims to recognize individuals across video sequences. Existing methods primarily use Euclidean space for representation learning but struggle to capture complex hierarchical structures, especially in scenarios with occlusions and background clutter. In contrast, hyperbolic space, with its negatively curved geometry, excels at preserving hierarchical relationships
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Learning with Enriched Inductive Biases for Vision-Language Models Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-28 Lingxiao Yang, Ru-Yuan Zhang, Qi Chen, Xiaohua Xie
Vision-Language Models, pre-trained on large-scale image-text pairs, serve as strong foundation models for transfer learning across a variety of downstream tasks. For few-shot generalization tasks, i.e., when the model is trained on few-shot samples and then tested on unseen categories or datasets, there is a balance to be struck between generalization and discrimination when tweaking these models
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Sample-Cohesive Pose-Aware Contrastive Facial Representation Learning Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-28 Yuanyuan Liu, Shaoze Feng, Shuyang Liu, Yibing Zhan, Dapeng Tao, Zijing Chen, Zhe Chen
Self-supervised facial representation learning (SFRL) methods, especially contrastive learning (CL) methods, have been increasingly popular due to their ability to perform face understanding without heavily relying on large-scale well-annotated datasets. However, analytically, current CL-based SFRL methods still perform unsatisfactorily in learning facial representations due to their tendency to learn
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SARATR-X: Towards Building A Foundation Model for SAR Target Recognition IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-28 Weijie Li, Wei Yang, Yuenan Hou, Li Liu, Yongxiang Liu, Xiang Li
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Leveraging Mixture Alignment for Multi-Source Domain Adaptation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-28 Aveen Dayal, S. Shrusti, Linga Reddy Cenkeramaddi, C. Krishna Mohan, Abhinav Kumar
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Vehicle wheel load positioning method based on multiple projective planes Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-28 Kai Sun, Xu Jiang, Xuhong Qiang
Computer vision‐based vehicle load monitoring methods could obtain spatiotemporal data of vehicle loads, which is important for bridge monitoring and operation. However, during the process of vehicle detection and tracking, current research usually focuses on the vehicle as a whole, and there is a lack of research on the accurate positioning of vehicle wheel loads. For the fatigue analysis of orthotropic
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Reinforcement learning‐based trajectory planning for continuous digging of excavator working devices in trenching tasks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-28 X. Tan, W. Wei, C. Liu, K. Cheng, Y. Wang, Z. Yao, Q. Huang
This paper addresses the challenge of real‐time, continuous trajectory planning for autonomous excavation. A hybrid method combining particle swarm optimization (PSO) and reinforcement learning (RL) is proposed. First, three types of excavation trajectories are defined for different geometric shapes of the digging area. Then, an excavation trajectory optimization method based on the PSO algorithm is
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Not Every Patch is Needed: Toward a More Efficient and Effective Backbone for Video-Based Person Re-Identification IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-27 Lanyun Zhu, Tianrun Chen, Deyi Ji, Jieping Ye, Jun Liu
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Transductive Few-Shot Learning With Enhanced Spectral-Spatial Embedding for Hyperspectral Image Classification IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-27 Bobo Xi, Yun Zhang, Jiaojiao Li, Yan Huang, Yunsong Li, Zan Li, Jocelyn Chanussot
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Modeling the collective behavior of pedestrians with the spontaneous loose leader–follower structure in public spaces Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-27 Jie Xu, Dengyu Xu, Jing Wu, Xiaowei Shi
Gaining insights into pedestrian flow patterns in public spaces can greatly benefit decision‐making processes related to infrastructure planning. Interestingly, even pedestrians are unfamiliar with one another, they often follow others, drawing on positive information and engaging in a spontaneous collective behavior of pedestrians. To model this collective behavior, this paper proposed a social force‐based
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SeaFormer++: Squeeze-Enhanced Axial Transformer for Mobile Visual Recognition Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-25 Qiang Wan, Zilong Huang, Jiachen Lu, Gang Yu, Li Zhang
Since the introduction of Vision Transformers, the landscape of many computer vision tasks (e.g., semantic segmentation), which has been overwhelmingly dominated by CNNs, recently has significantly revolutionized. However, the computational cost and memory requirement renders these methods unsuitable on the mobile device. In this paper, we introduce a new method squeeze-enhanced Axial Transformer (SeaFormer)
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Automated construction contract analysis for risk and responsibility assessment using natural language processing and machine learning Comput. Ind. (IF 8.2) Pub Date : 2025-01-25 Irem Dikmen, Gorkem Eken, Huseyin Erol, M. Talat Birgonul
Construction contracts contain critical risk-related information that requires in-depth examination, yet tight schedules for bidding limit the possibility of comprehensive review of extensive documents manually. This research aims to develop models for automating the review of construction contracts to extract information on risk and responsibility that will provide inputs for risk management plans
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DeepFake-Adapter: Dual-Level Adapter for DeepFake Detection Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-24 Rui Shao, Tianxing Wu, Liqiang Nie, Ziwei Liu
Existing deepfake detection methods fail to generalize well to unseen or degraded samples, which can be attributed to the over-fitting of low-level forgery patterns. Here we argue that high-level semantics are also indispensable recipes for generalizable forgery detection. Recently, large pre-trained Vision Transformers (ViTs) have shown promising generalization capability. In this paper, we propose
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MoonShot: Towards Controllable Video Generation and Editing with Motion-Aware Multimodal Conditions Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-24 David Junhao Zhang, Dongxu Li, Hung Le, Mike Zheng Shou, Caiming Xiong, Doyen Sahoo
Current video diffusion models (VDMs) mostly rely on text conditions, limiting control over video appearance and geometry. This study introduces a new model, MoonShot, conditioning on both image and text for enhanced control. It features the Multimodal Video Block (MVB), integrating the motion-aware dual cross-attention layer for precise appearance and motion alignment with provided prompts, and the
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Local Uncertainty Energy Transfer for Active Domain Adaptation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-24 Yulin Sun, Guangming Shi, Weisheng Dong, Xin Li, Le Dong, Xuemei Xie
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Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-24 Shengxun Wei, Zan Gao, Chunjie Ma, Yibo Zhao, Weili Guan, Shengyong Chen
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Enhanced three‐dimensional instance segmentation using multi‐feature extracting point cloud neural network Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-24 Hongxu Wang, Jiepeng Liu, Dongsheng Li, Tianze Chen, Pengkun Liu, Han Yan, Yadong Wu
Precise three‐dimensional (3D) instance segmentation of indoor scenes plays a critical role in civil engineering, including reverse engineering, size detection, and advanced structural analysis. However, existing methods often fall short in accurately segmenting complex indoor environments due to challenges of diverse material textures, irregular object shapes, and inadequate datasets. To address these
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A Mutual Supervision Framework for Referring Expression Segmentation and Generation Int. J. Comput. Vis. (IF 11.6) Pub Date : 2025-01-23 Shijia Huang, Feng Li, Hao Zhang, Shilong Liu, Lei Zhang, Liwei Wang
Reference Expression Segmentation (RES) and Reference Expression Generation (REG) are mutually inverse tasks that can be naturally jointly trained. Though recent work has explored such joint training, the mechanism of how RES and REG can benefit each other is still unclear. In this paper, we propose a mutual supervision framework that enables two tasks to improve each other. Our mutual supervision
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TRTST: Arbitrary High-Quality Text-Guided Style Transfer with Transformers IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-23 Haibo Chen, Zhoujie Wang, Lei Zhao, Jun Li, Jian Yang
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Addressing inconsistent labeling with cross image matching for scribble-based medical image segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-23 Jingkun Chen, Wenjian Huang, Jianguo Zhang, Kurt Debattista, Jungong Han
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Dense Information Learning based Semi-Supervised Object Detection IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-23 Xi Yang, Penghui Li, Qiubai Zhou, Nannan Wang, Xinbo Gao
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Onet: Twin U-Net Architecture for Unsupervised Binary Semantic Segmentation in Radar and Remote Sensing Images IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-23 Yi Zhou, Hang Su, Tian Wang, Qing Hu
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TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-23 Xiaoyan Cao, Yiyao Zheng, Yao Yao, Huapeng Qin, Xiaoyu Cao, Shihui Guo
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GCSTG: Generating Class-confusion-aware Samples with a Tree-structure Graph for Few-shot Object Detection IEEE Trans. Image Process. (IF 10.8) Pub Date : 2025-01-23 Longrong Yang, Hanbin Zhao, Hongliang Li, Liang Qiao, Ziwei Yang, Xi Li
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