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Cross-Attention Regression Flow for Defect Detection. IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-19 Binhui Liu,Tianchu Guo,Bin Luo,Zhen Cui,Jian Yang
Defect detection from images is a crucial and challenging topic of industry scenarios due to the scarcity and unpredictability of anomalous samples. However, existing defect detection methods exhibit low detection performance when it comes to small-size defects. In this work, we propose a Cross-Attention Regression Flow (CARF) framework to model a compact distribution of normal visual patterns for
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SIM-OFE: Structure Information Mining and Object-aware Feature Enhancement for Fine-Grained Visual Categorization IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-18 Hongbo Sun, Xiangteng He, Jinglin Xu, Yuxin Peng
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Dual Consensus Anchor Learning for Fast Multi-view Clustering IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-18 Yalan Qin, Chuan Qin, Xinpeng Zhang, Guorui Feng
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Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-18 Junyu Gao, Xinhong Ma, Changsheng Xu
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Recalling Unknowns without Losing Precision: An Effective Solution to Large Model-Guided Open World Object Detection IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-18 Yulin He, Wei Chen, Siqi Wang, Tianrui Liu, Meng Wang
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To Boost Zero-shot Generalization for Embodied Reasoning with Vision-Language Pre-training IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-18 Ke Su, Xingxing Zhang, Siyang Zhang, Jun Zhu, Bo Zhang
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CWSCNet: Channel-Weighted Skip Connection Network for Underwater Object Detection IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-18 Long Chen, Yunzhou Xie, Yaxin Li, Qi Xu, Junyu Dong
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Adapting Few-shot Classification via In-process Defense IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-17 Xi Yang, Dechen Kong, Ren Lin, Nannan Wang, Xinbo Gao
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Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-17 Bo Dang, Yansheng Li, Yongjun Zhang, Jiayi Ma
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Simultaneous temperature estimation and nonuniformity correction from multiple frames IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-17 Navot Oz, Omri Berman, Nir Sochen, David Mendelovich, Iftach Klapp
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Adaptive Log-Euclidean Metrics for SPD Matrix Learning IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-16 Ziheng Chen, Yue Song, Tianyang Xu, Zhiwu Huang, Xiao-Jun Wu, Nicu Sebe
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Neural Degradation Representation Learning for All-In-One Image Restoration IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-13 Mingde Yao, Ruikang Xu, Yuanshen Guan, Jie Huang, Zhiwei Xiong
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Change Representation and Extraction in Stripes: Rethinking Unsupervised Hyperspectral Image Change Detection With an Untrained Network IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-13 Bin Yang, Yin Mao, Licheng Liu, Leyuan Fang, Xinxin Liu
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Disentangled Sample Guidance Learning for Unsupervised Person Re-identification IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-12 Haoxuanye Ji, Le Wang, Sanping Zhou, Wei Tang, Gang Hua
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Image-Level Adaptive Adversarial Ranking for Person Re-identification IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-12 Xi Yang, Huanling Liu, Nannan Wang, Xinbo Gao
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UniParser: Multi-Human Parsing with Unified Correlation Representation Learning IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-12 Jiaming Chu, Lei Jin, YingLei Teng, Jianshu Li, Yunchao Wei, Zheng Wang, Junliang Xing, Shuicheng Yan, Jian Zhao
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Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-12 Somdyuti Paul, Andrey Norkin, Alan C. Bovik
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Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-11 Hanxi Li, Jianfei Hu, Bo Li, Hao Chen, Yongbin Zheng, Chunhua Shen
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TMP: Temporal Motion Propagation for Online Video Super-Resolution IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-09 Zhengqiang Zhang, Ruihuang Li, Shi Guo, Yang Cao, Lei Zhang
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Unified and Real-Time Image Geo-Localization via Fine-grained Overlap Estimation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-09 Ze Song, Xudong Kang, Xiaohui Wei, Shutao Li, Haibo Liu
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Modelling of Multiple Spatial-Temporal Relations for Robust Visual Object Tracking IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-09 Shilei Wang, Zhenhua Wang, Qianqian Sun, Gong Cheng, Jifeng Ning
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HeightFormer: Explicit Height Modeling without Extra Data for Camera-only 3D Object Detection in Bird’s Eye View IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-09 Yiming Wu, Ruixiang Li, Zequn Qin, Xinhai Zhao, Xi Li
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Learning a Non-Locally Regularized Convolutional Sparse Representation for Joint Chromatic and Polarimetric Demosaicking IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-09 Yidong Luo, Junchao Zhang, Jianbo Shao, Jiandong Tian, Jiayi Ma
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Style Consistency Unsupervised Domain Adaptation Medical Image Segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Lang Chen, Yun Bian, Jianbin Zeng, Qingquan Meng, Weifang Zhu, Fei Shi, Chengwei Shao, Xinjian Chen, Dehui Xiang
Unsupervised domain adaptation medical image segmentation is aimed to segment unlabeled target domain images with labeled source domain images. However, different medical imaging modalities lead to large domain shift between their images, in which well-trained models from one imaging modality often fail to segment images from anothor imaging modality. In this paper, to mitigate domain shift between
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Generating Stylized Features for Single-Source Cross-Dataset Palmprint Recognition With Unseen Target Dataset IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Huikai Shao, Pengxu Li, Dexing Zhong
As a promising topic in palmprint recognition, cross-dataset palmprint recognition is attracting more and more research interests. In this paper, a more difficult yet realistic scenario is studied, i.e., Single-Source Cross-Dataset Palmprint Recognition with Unseen Target dataset (S2CDPR-UT). It is aimed to generalize a palmprint feature extractor trained only on a single source dataset to multiple
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M2GCNet: Multi-Modal Graph Convolution Network for Precise Brain Tumor Segmentation Across Multiple MRI Sequences IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Tongxue Zhou
Accurate segmentation of brain tumors across multiple MRI sequences is essential for diagnosis, treatment planning, and clinical decision-making. In this paper, I propose a cutting-edge framework, named multi-modal graph convolution network (M2GCNet), to explore the relationships across different MR modalities, and address the challenge of brain tumor segmentation. The core of M2GCNet is the multi-modal
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Balanced Destruction-Reconstruction Dynamics for Memory-Replay Class Incremental Learning IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Yuhang Zhou, Jiangchao Yao, Feng Hong, Ya Zhang, Yanfeng Wang
Class incremental learning (CIL) aims to incrementally update a trained model with the new classes of samples (plasticity) while retaining previously learned ability (stability). To address the most challenging issue in this goal, i.e., catastrophic forgetting, the mainstream paradigm is memory-replay CIL, which consolidates old knowledge by replaying a small number of old classes of samples saved
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Privacy-Preserving Autoencoder for Collaborative Object Detection IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Bardia Azizian, Ivan V. Bajić
Privacy is a crucial concern in collaborative machine vision where a part of a Deep Neural Network (DNN) model runs on the edge, and the rest is executed on the cloud. In such applications, the machine vision model does not need the exact visual content to perform its task. Taking advantage of this potential, private information could be removed from the data insofar as it does not significantly impair
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Structural Relation Modeling of 3D Point Clouds IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Yu Zheng, Jiwen Lu, Yueqi Duan, Jie Zhou
In this paper, we propose an effective plug-and-play module called structural relation network (SRN) to model structural dependencies in 3D point clouds for feature representation. Existing network architectures such as PointNet++ and RS-CNN capture local structures individually and ignore the inner interactions between different sub-clouds. Motivated by the fact that structural relation modeling plays
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Reference-Based Multi-Stage Progressive Restoration for Multi-Degraded Images IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Yi Zhang, Qixue Yang, Damon M. Chandler, Xuanqin Mou
Image restoration (IR) via deep learning has been vigorously studied in recent years. However, due to the ill-posed nature of the problem, it is challenging to recover the high-quality image details from a single distorted input especially when images are corrupted by multiple distortions. In this paper, we propose a multi-stage IR approach for progressive restoration of multi-degraded images via transferring
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Blind Video Quality Prediction by Uncovering Human Video Perceptual Representation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Liang Liao, Kangmin Xu, Haoning Wu, Chaofeng Chen, Wenxiu Sun, Qiong Yan, C.-C. Jay Kuo, Weisi Lin
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Contrastive Open-set Active Learning based Sample Selection for Image Classification IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Zizheng Yan, Delian Ruan, Yushuang Wu, Junshi Huang, Zhenhua Chai, Xiaoguang Han, Shuguang Cui, Guanbin Li
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UVaT: Uncertainty Incorporated View-aware Transformer for Robust Multi-view Classification IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Yapeng Li, Yong Luo, Bo Du
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Rethinking Self-Training for Semi-Supervised Landmark Detection: A Selection-Free Approach IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Haibo Jin, Haoxuan Che, Hao Chen
Self-training is a simple yet effective method for semi-supervised learning, during which pseudo-label selection plays an important role for handling confirmation bias. Despite its popularity, applying self-training to landmark detection faces three problems: 1) The selected confident pseudo-labels often contain data bias, which may hurt model performance; 2) It is not easy to decide a proper threshold
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Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-05 Yujiang Pu, Xiaoyu Wu, Lulu Yang, Shengjin Wang
Weakly supervised video anomaly detection aims to locate abnormal activities in untrimmed videos without the need for frame-level supervision. Prior work has utilized graph convolution networks or self-attention mechanisms alongside multiple instance learning (MIL)-based classification loss to model temporal relations and learn discriminative features. However, these approaches are limited in two aspects:
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Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-09-02 Tianshui Chen, Weihang Wang, Tao Pu, Jinghui Qin, Zhijing Yang, Jie Liu, Liang Lin
Modern visual recognition models often display overconfidence due to their reliance on complex deep neural networks and one-hot target supervision, resulting in unreliable confidence scores that necessitate calibration. While current confidence calibration techniques primarily address single-label scenarios, there is a lack of focus on more practical and generalizable multi-label contexts. This paper
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IdeNet: Making Neural Network Identify Camouflaged Objects Like Creatures IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-30 Juwei Guan, Xiaolin Fang, Tongxin Zhu, Zhipeng Cai, Zhen Ling, Ming Yang, Junzhou Luo
Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only roughly define the general process, which deficiently reproduces the process of identifying camouflaged objects. How to make modeled neural networks perceive
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Beyond Appearance: Multi-Frame Spatio-Temporal Context Memory Networks for Efficient and Robust Video Object Segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-29 Jisheng Dang, Huicheng Zheng, Xiaohao Xu, Longguang Wang, Yulan Guo
Current video object segmentation approaches primarily rely on frame-wise appearance information to perform matching. Despite significant progress, reliable matching becomes challenging due to rapid changes of the object’s appearance over time. Moreover, previous matching mechanisms suffer from redundant computation and noise interference as the number of accumulated frames increases. In this paper
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RoMo: Robust Unsupervised Multimodal Learning with Noisy Pseudo Labels IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-27 Yongxiang Li, Yang Qin, Yuan Sun, Dezhong Peng, Xi Peng, Peng Hu
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Spatio-Temporal Convolutional Neural Network for Enhanced Inter Prediction in Video Coding IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-26 Philipp Merkle, Martin Winken, Jonathan Pfaff, Heiko Schwarz, Detlev Marpe, Thomas Wiegand
This paper presents a convolutional neural network (CNN)-based enhancement to inter prediction in Versatile Video Coding (VVC). Our approach aims at improving the prediction signal of inter blocks with a residual CNN that incorporates spatial and temporal reference samples. It is motivated by the theoretical consideration that neural network-based methods have a higher degree of signal adaptivity than
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Decouple Ego-View Motions for Predicting Pedestrian Trajectory and Intention IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-26 Zhengming Zhang, Zhengming Ding, Renran Tian
Pedestrian trajectory prediction is a critical component of autonomous driving in urban environments, allowing vehicles to anticipate pedestrian movements and facilitate safer interactions. While egocentric-view-based algorithms can reduce the sensing and computation burdens of 3D scene reconstruction, accurately predicting pedestrian trajectories and interpreting their intentions from this perspective
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Dual Contrast-Driven Deep Multi-View Clustering IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-26 Jinrong Cui, Yuting Li, Han Huang, Jie Wen
Consensus representation learning is one of the most popular approaches in the field of multi-view clustering. However, most of the existing methods cannot learn discriminative representations with a clustering-friendly structure since these methods ignore the separation among clusters and the compactness within each cluster. To tackle this issue, we propose a new deep multi-view clustering network
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Facial Action Unit Representation based on Self-supervised Learning with Ensembled Priori Constraints IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-26 Haifeng Chen, Peng Zhang, Chujia Guo, Ke Lu, Dongmei Jiang
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Relation Knowledge Distillation by Auxiliary Learning for Object Detection IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-26 Hao Wang, Tong Jia, Qilong Wang, Wangmeng Zuo
Balancing the trade-off between accuracy and speed for obtaining higher performance without sacrificing the inference time is a challenging topic for object detection task. Knowledge distillation, which serves as a kind of model compression techniques, provides a potential and feasible way to handle above efficiency and effectiveness issue through transferring the dark knowledge from the sophisticated
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Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-26 Haisheng Fu, Feng Liang, Jie Liang, Yongqiang Wang, Zhenman Fang, Guohe Zhang, Jingning Han
Deep learning-based image compression has made great progresses recently. However, some leading schemes use serial context-adaptive entropy model to improve the rate-distortion (R-D) performance, which is very slow. In addition, the complexities of the encoding and decoding networks are quite high and not suitable for many practical applications. In this paper, we propose four techniques to balance
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Joint Under-Sampling Pattern and Dual-Domain Reconstruction for Accelerating Multi-Contrast MRI IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-23 Pengcheng Lei, Le Hu, Faming Fang, Guixu Zhang
Multi-Contrast Magnetic Resonance Imaging (MCMRI) utilizes the short-time reference image to facilitate the reconstruction of the long-time target one, providing a new solution for fast MRI. Although various methods have been proposed, they still have certain limitations. 1) existing methods featuring the preset under-sampling patterns give rise to redundancy between multi-contrast images and limit
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Segmentation and Completion of Human Motion Sequence via Temporal Learning of Subspace Variety Model IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-23 Zheng Xing, Weibing Zhao
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Low-Light Phase Retrieval With Implicit Generative Priors IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-23 Raunak Manekar, Elisa Negrini, Minh Pham, Daniel Jacobs, Jaideep Srivastava, Stanley J. Osher, Jianwei Miao
Phase retrieval (PR) is fundamentally important in scientific imaging and is crucial for nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is essential for applications involving radiation-sensitive samples. However, most PR methods struggle in low-dose scenarios due to high shot noise. Recent advancements in optical data acquisition setups, such as in-situ CDI
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Line-Based 6-DoF Object Pose Estimation and Tracking With an Event Camera IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-23 Zibin Liu, Banglei Guan, Yang Shang, Qifeng Yu, Laurent Kneip
Pose estimation and tracking of objects is a fundamental application in 3D vision. Event cameras possess remarkable attributes such as high dynamic range, low latency, and resilience against motion blur, which enables them to address challenging high dynamic range scenes or high-speed motion. These features make event cameras an ideal complement over standard cameras for object pose estimation. In
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Learning to Generate Parameters of ConvNets for Unseen Image Data IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-23 Shiye Wang, Kaituo Feng, Changsheng Li, Ye Yuan, Guoren Wang
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Image Super-Resolution via Efficient Transformer Embedding Frequency Decomposition With Restart IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-21 Yifan Zuo, Wenhao Yao, Yuqi Hu, Yuming Fang, Wei Liu, Yuxin Peng
Recently, transformer-based backbones show superior performance over the convolutional counterparts in computer vision. Due to quadratic complexity with respect to the token number in global attention, local attention is always adopted in low-level image processing with linear complexity. However, the limited receptive field is harmful to the performance. In this paper, motivated by Octave convolution
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Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-21 Ye Du, Zehua Fu, Qingjie Liu
Recent attention has been devoted to the pursuit of learning semantic segmentation models exclusively from image tags, a paradigm known as image-level Weakly Supervised Semantic Segmentation (WSSS). Existing attempts adopt the Class Activation Maps (CAMs) as priors to mine object regions yet observe the imbalanced activation issue, where only the most discriminative object parts are located. In this
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Scalable and Structural Multi-View Graph Clustering With Adaptive Anchor Fusion IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-21 Siwei Wang, Xinwang Liu, Suyuan Liu, Wenxuan Tu, En Zhu
Anchor graph has been recently proposed to accelerate multi-view graph clustering and widely applied in various large-scale applications. Different from capturing full instance relationships, these methods choose small portion anchors among each view, construct single-view anchor graphs and combine them into the unified graph. Despite its efficiency, we observe that: (i) Existing mechanism adopts a
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Fast Projected Fuzzy Clustering With Anchor Guidance for Multimodal Remote Sensing Imagery IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-21 Yongshan Zhang, Shuaikang Yan, Lefei Zhang, Bo Du
Multimodal remote sensing image recognition is a popular research topic in the field of remote sensing. This recognition task is mostly solved by supervised learning methods that heavily rely on manually labeled data. When the labels are absent, the recognition is challenging for the large data size, complex land-cover distribution and large modality spectrum variation. In this paper, a novel unsupervised
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Low Overlapping Point Cloud Registration Using Mutual Prior Based Completion Network IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-20 Yazhou Liu, Zhiyong Liu
This work presents a new completion method that specifically designed for low-overlapping partial point cloud registration. Based on the assumption that the candidate partial point clouds to be registered belong to the same target, the proposed mutual prior based completion (MPC) method uses these candidate partial point clouds as completion reference for each other to extend their overlapping regions
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Adaptive Prototype Learning for Weakly-supervised Temporal Action Localization IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-20 Wang Luo, Huan Ren, Tianzhu Zhang, Wenfei Yang, Yongdong Zhang
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Deep Cross-View Reconstruction GAN Based on Correlated Subspace for Multi-View Transformation IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-19 Jian-Xun Mi, Junchang He, Weisheng Li
In scenarios where identifying face information in the visible spectrum (VIS) is challenging due to poor lighting conditions, the use of near-infrared (NIR) and thermal (TH) cameras can provide viable alternatives. However, the unique data distribution of images captured by these cameras compared to VIS images presents challenges in matching face identities. To address these challenges, we propose
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Lightweight Prompt Learning Implicit Degradation Estimation Network for Blind Super Resolution IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-19 Asif Hussain Khan, Christian Micheloni, Niki Martinel
Blind image super-resolution (SR) aims to recover a high-resolution (HR) image from its low-resolution (LR) counterpart under the assumption of unknown degradations. Many existing blind SR methods rely on supervising ground-truth kernels referred to as explicit degradation estimators. However, it is very challenging to obtain the ground-truths for different degradations kernels. Moreover, most of these
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A Trustworthy Counterfactual Explanation Method With Latent Space Smoothing IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-19 Yan Li, Xia Cai, Chunwei Wu, Xiao Lin, Guitao Cao
Despite the large-scale adoption of Artificial Intelligence (AI) models in healthcare, there is an urgent need for trustworthy tools to rigorously backtrack the model decisions so that they behave reliably. Counterfactual explanations take a counter-intuitive approach to allow users to explore “what if” scenarios gradually becoming popular in the trustworthy field. However, most previous work on model’s
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Linear Combinations of Patches are Unreasonably Effective for Single-Image Denoising IEEE Trans. Image Process. (IF 10.8) Pub Date : 2024-08-19 Sébastien Herbreteau, Charles Kervrann
In the past decade, deep neural networks have revolutionized image denoising in achieving significant accuracy improvements by learning on datasets composed of noisy/clean image pairs. However, this strategy is extremely dependent on training data quality, which is a well-established weakness. To alleviate the requirement to learn image priors externally, single-image (a.k.a., self-supervised or zero-shot)