Query：
学者姓名：陈飞
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Image manipulation has sparked widespread concern due to its potential security threats on the Internet. The boundary between the authentic and manipulated region exhibits artifacts in image manipulation localization (IML). These artifacts are more pronounced in heterogeneous image splicing and homogeneous image copy-move manipulation, while they are more subtle in removal and inpainting manipulated images. However, existing methods for image manipulation detection tend to capture boundary artifacts via explicit edge features and have limitations in effectively addressing subtle artifacts. Besides, feature redundancy caused by the powerful feature extraction capability of large models may prevent accurate identification of manipulated artifacts, exhibiting a high false-positive rate. To solve these problems, we propose a novel edge-aware network (EAN) to capture boundary artifacts effectively. This network treats the image manipulation localization problem as a segmentation problem inside and outside the boundary. In EAN, we develop an edge-aware mechanism to refine implicit and explicit edge features by the interaction of adjacent features. This approach directs the encoder to prioritize the desired edge information. Also, we design a multi-feature fusion strategy combined with an improved attention mechanism to enhance key feature representation significantly for mitigating the effects of feature redundancy. We perform thorough experiments on diverse datasets, and the outcomes confirm the efficacy of the suggested approach, surpassing leading manipulation localization techniques in the majority of scenarios.
Keyword ：
attention mechanism attention mechanism Attention mechanisms Attention mechanisms convolutional neural network convolutional neural network Discrete wavelet transforms Discrete wavelet transforms Feature extraction Feature extraction feature fusion feature fusion Image edge detection Image edge detection Image manipulation localization Image manipulation localization Location awareness Location awareness Neural networks Neural networks Noise Noise Semantics Semantics Splicing Splicing Transformers Transformers
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Yun , Cheng, Hang , Wang, Haichou et al. EAN: Edge-Aware Network for Image Manipulation Localization [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) : 1591-1601 . |
| MLA | Chen, Yun et al. "EAN: Edge-Aware Network for Image Manipulation Localization" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35 . 2 (2025) : 1591-1601 . |
| APA | Chen, Yun , Cheng, Hang , Wang, Haichou , Liu, Ximeng , Chen, Fei , Li, Fengyong et al. EAN: Edge-Aware Network for Image Manipulation Localization . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) , 1591-1601 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
While deep learning techniques, such as Convolutional neural networks (CNNs), show significant potential in medical applications, real-time detection of parathyroid glands (PGs) during complex surgeries remains insufficiently explored, posing challenges for surgical accuracy and outcomes. Previous studies highlight the importance of leveraging prior knowledge, such as shape, for feature extraction in detection tasks. However, they fail to address the critical multi-scale variability of PG objects, resulting in suboptimal performance and efficiency. In this paper, we propose an end-to-end framework, MSWF-PGD, for Multi-Scale Weighted Fusion Parathyroid Gland Detection. To improve accuracy and efficiency, our approach extracts feature maps from convolutional layers at multiple scales and re-weights them using cluster-aware multi-scale alignment, considering diverse attributes such as the size, color, and position of PGs. Additionally, we introduce Multi-Scale Aggregation to enhance scale interactions and enable adaptive multi-scale feature fusion, providing precise and informative locality information for detection. Extensive comparative experiments and ablation studies on the parathyroid dataset (PGsdata) demonstrate the proposed framework's superiority in accuracy and real-time efficiency, outperforming state-of-the-art models such as RetinaNet, FCOS, and YOLOv8.
Keyword ：
feature fusion feature fusion multi-scale features multi-scale features object detection object detection parathyroid glands parathyroid glands prior information prior information
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Wanling , Lu, Wenhuan , Li, Yijian et al. Parathyroid Gland Detection Based on Multi-Scale Weighted Fusion Attention Mechanism [J]. | ELECTRONICS , 2025 , 14 (6) . |
| MLA | Liu, Wanling et al. "Parathyroid Gland Detection Based on Multi-Scale Weighted Fusion Attention Mechanism" . | ELECTRONICS 14 . 6 (2025) . |
| APA | Liu, Wanling , Lu, Wenhuan , Li, Yijian , Chen, Fei , Jiang, Fan , Wei, Jianguo et al. Parathyroid Gland Detection Based on Multi-Scale Weighted Fusion Attention Mechanism . | ELECTRONICS , 2025 , 14 (6) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Existing vision-language pre-training models typically extract region features and conduct fine-grained local alignment based on masked image/text completion or object detection methods. However, these models often design independent subtasks for different modalities, which may not adequately leverage interactions between modalities, requiring large datasets to achieve optimal performance. To address these limitations, this paper introduces a novel pre-training approach that facilitates fine-grained vision-language interaction. We propose two new subtasks — image filling and text filling — that utilize data from one modality to complete missing parts in another, enhancing the model's ability to integrate multi-modal information. A selector mechanism is also developed to minimize semantic overlap between modalities, thereby improving the efficiency and effectiveness of the pre-trained model. Our comprehensive experimental results demonstrate that our approach not only fosters better semantic associations among different modalities but also achieves state-of-the-art performance on downstream vision-language tasks with significantly smaller datasets. © 2024 Elsevier Ltd
Keyword ：
Cross-modal Cross-modal Image captioning Image captioning Partial auxiliary Partial auxiliary Pre-training Pre-training
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Cheng, H. , Ye, H. , Zhou, X. et al. Vision-language pre-training via modal interaction [J]. | Pattern Recognition , 2024 , 156 . |
| MLA | Cheng, H. et al. "Vision-language pre-training via modal interaction" . | Pattern Recognition 156 (2024) . |
| APA | Cheng, H. , Ye, H. , Zhou, X. , Liu, X. , Chen, F. , Wang, M. . Vision-language pre-training via modal interaction . | Pattern Recognition , 2024 , 156 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Due to the existence of small inter-class differences and large intra-class variance among fine-grained images, the existing classification algorithms only focus on the extraction and representation learning of salient local features of a single image, ignoring the local heterogeneous semantic discrimination information between multiple images, difficult to pay attention to the subtle details that distinguish different categories, resulting in the lack of sufficient discrimination of the learned features. This paper proposes a progressive network to learn the information of different granularity levels of the image in a weakly supervised manner. First, attention accumulation object localization module (AAOLM) is constructed to perform semantic target integration localization on attention information from different training epochs and feature extraction stages on a single image. Second, a multi-image heterogeneous local interactive graph module (HLIGM) is designed to construct a graph network and aggregate information between the local region features of multiple images under the guidance of the category label after extracting the salient local region features of each image to enhance the discriminative power of the representation. Finally, the optimization information generated by HLIGM is fed back to the backbone by using knowledge distillation so that it can directly extract features with strong discrimination, avoiding the computational overhead of building the graph in the test phase. Through experiments on multiple data sets, it proves the effectiveness of the proposed method, which can improve the fine-grained classification accuracy. © 2024 Science Press. All rights reserved.
Keyword ：
Deep neural networks Deep neural networks Graph neural networks Graph neural networks Image enhancement Image enhancement Image representation Image representation Knowledge graph Knowledge graph Self-supervised learning Self-supervised learning Semantic Segmentation Semantic Segmentation Supervised learning Supervised learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Quan , Chen, Fei , Wang, Yan-Gen et al. Fine-grained Image Classification by Integrating Object Localization and Heterogeneous Local Interactive Learning [J]. | Acta Automatica Sinica , 2024 , 50 (11) : 2219-2230 . |
| MLA | Chen, Quan et al. "Fine-grained Image Classification by Integrating Object Localization and Heterogeneous Local Interactive Learning" . | Acta Automatica Sinica 50 . 11 (2024) : 2219-2230 . |
| APA | Chen, Quan , Chen, Fei , Wang, Yan-Gen , Cheng, Hang , Wang, Mei-Qing . Fine-grained Image Classification by Integrating Object Localization and Heterogeneous Local Interactive Learning . | Acta Automatica Sinica , 2024 , 50 (11) , 2219-2230 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
We revisit the well-studied image segmentation problem from a soft labeling perspective: instead of estimating integer labels per pixel indicating a finite set of classes, each pixel is assigned a real number that conveys the level of uncertainty in the estimated class label. Soft labels are useful, for example, for subsequent human editing or composition. Specifically, given a set of pre-computed super-pixel labels and feature vectors per pixel, we formulate a convex optimization objective regularized by signal-dependent gradient graph Laplacian regularizers (GGLR), which promotes piecewise planar (PWP) signal reconstruction. Unlike a previous well-known soft segmentation scheme that requires expensive computation of the first 100 eigenvectors, our optimization can be solved efficiently in linear time via conjugate gradient (CG). Experimental results show that our method produces satisfactory soft labels per pixel for images in two public datasets at a reduced computation cost compared to the previous soft segmentation scheme.
Keyword ：
gradient graph Laplacian regularizer gradient graph Laplacian regularizer graph signal processing graph signal processing Image segmentation Image segmentation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Fei , Cheung, Gene , Zhang, Xue . SOFT IMAGE SEGMENTATION USING GRADIENT GRAPH LAPLACIAN REGULARIZER [J]. | 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2024) , 2024 : 9526-9530 . |
| MLA | Chen, Fei et al. "SOFT IMAGE SEGMENTATION USING GRADIENT GRAPH LAPLACIAN REGULARIZER" . | 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2024) (2024) : 9526-9530 . |
| APA | Chen, Fei , Cheung, Gene , Zhang, Xue . SOFT IMAGE SEGMENTATION USING GRADIENT GRAPH LAPLACIAN REGULARIZER . | 2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2024) , 2024 , 9526-9530 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Obtaining pixel-accurate and topologically complete retinal vessel segmentation is challenging due to many factors, for instance, vascular structure complexity, image contrast variations, and limitations of valuable datasets. In this paper, we introduce a novel network structure that applies dual-branch: directional reweighted branch and skeletonized branch. In the direction reweighted branch, we propose adaptive directional enhancement and connectivity consistency enhancement, which can be used to extract favorable directional channel information and model the bidirectional relationship between pixels, respectively. In the skeletonized branch, we employ morphological skeletonization to align the ground truth with the predicted segmentation map. By doing that, we effectively preserve the vessel's topological structure from a global perspective. Extensive experiments on publicly available retinal datasets DRIVE, CHASE_DB1, and STARE show that our proposed approach has achieved significant results in preserving vessel structure and accurate segmentation. © 2024 IEEE.
Keyword ：
Eye protection Eye protection Face recognition Face recognition Image segmentation Image segmentation Musculoskeletal system Musculoskeletal system Topology Topology
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | He, Zejun , Chen, Fei , Jiang, Fan et al. A Dual-Branch Network Based on Connectivity Mask for Retinal Vessel Segmentation [C] . 2024 . |
| MLA | He, Zejun et al. "A Dual-Branch Network Based on Connectivity Mask for Retinal Vessel Segmentation" . (2024) . |
| APA | He, Zejun , Chen, Fei , Jiang, Fan , Liu, Wanling , Ye, Zhangyan . A Dual-Branch Network Based on Connectivity Mask for Retinal Vessel Segmentation . (2024) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Existing vision-language pre-training models typically extract region features and conduct fine-grained local alignment based on masked image/text completion or object detection methods. However, these models often design independent subtasks for different modalities, which may not adequately leverage interactions between modalities, requiring large datasets to achieve optimal performance. To address these limitations, this paper introduces a novel pre-training approach that facilitates fine-grained vision-language interaction. We propose two new subtasks - image filling and text filling - that utilize data from one modality to complete missing parts in another, enhancing the model's ability to integrate multi-modal information. A selector mechanism is also developed to minimize semantic overlap between modalities, thereby improving the efficiency and effectiveness of the pre-trained model. Our comprehensive experimental results demonstrate that our approach not only fosters better semantic associations among different modalities but also achieves state-of-the-art performance on downstream vision-language tasks with significantly smaller datasets.
Keyword ：
Cross-modal Cross-modal Image captioning Image captioning Partial auxiliary Partial auxiliary Pre-training Pre-training
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Cheng, Hang , Ye, Hehui , Zhou, Xiaofei et al. Vision-language pre-training via modal interaction [J]. | PATTERN RECOGNITION , 2024 , 156 . |
| MLA | Cheng, Hang et al. "Vision-language pre-training via modal interaction" . | PATTERN RECOGNITION 156 (2024) . |
| APA | Cheng, Hang , Ye, Hehui , Zhou, Xiaofei , Liu, Ximeng , Chen, Fei , Wang, Meiqing . Vision-language pre-training via modal interaction . | PATTERN RECOGNITION , 2024 , 156 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
In the graph signal processing (GSP) literature, graph Laplacian regularizer (GLR) was used for signal restoration to promote piecewise smooth / constant reconstruction with respect to an underlying graph. However, for signals slowly varying across graph kernels, GLR suffers from an undesirable "staircase" effect. In this paper, focusing on manifold graphs-collections of uniform discrete samples on low-dimensional continuous manifolds-we generalize GLR to gradient graph Laplacian regularizer (GGLR) that promotes planar / piecewise planar (PWP) signal reconstruction. Specifically, for a graph endowed with sampling coordinates (e.g., 2D images, 3D point clouds), we first define a gradient operator, using which we construct a gradient graph for nodes' gradients in the sampling manifold space. This maps to a gradient-induced nodal graph (GNG) and a positive semi-definite (PSD) Laplacian matrix with planar signals as the 0 frequencies. For manifold graphs without explicit sampling coordinates, we propose a graph embedding method to obtain node coordinates via fast eigenvector computation. We derive the means-square-error minimizing weight parameter for GGLR efficiently, trading off bias and variance of the signal estimate. Experimental results show that GGLR outperformed previous graph signal priors like GLR and graph total variation (GTV) in a range of graph signal restoration tasks.
Keyword ：
graph embedding graph embedding Graph signal processing Graph signal processing graph smoothness priors graph smoothness priors quadratic programming quadratic programming
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Fei , Cheung, Gene , Zhang, Xue . Manifold Graph Signal Restoration Using Gradient Graph Laplacian Regularizer [J]. | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2024 , 72 : 744-761 . |
| MLA | Chen, Fei et al. "Manifold Graph Signal Restoration Using Gradient Graph Laplacian Regularizer" . | IEEE TRANSACTIONS ON SIGNAL PROCESSING 72 (2024) : 744-761 . |
| APA | Chen, Fei , Cheung, Gene , Zhang, Xue . Manifold Graph Signal Restoration Using Gradient Graph Laplacian Regularizer . | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2024 , 72 , 744-761 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The health-related Internet of Things (IoT) plays an irreplaceable role in the collection, analysis, and transmission of medical data. As a device of the health-related IoT, the electroencephalogram (EEG) has long been a powerful tool for physiological and clinical brain research, which contains a wealth of personal information. Due to its rich computational/storage resources, cloud computing is a promising solution to extract the sophisticated feature of massive EEG signals in the age of big data. However, it needs to solve both response latency and privacy leakage. To reduce latency between users and servers while ensuring data privacy, we propose a privacy-preserving feature extraction scheme, called LightPyFE, for EEG signals in the edge computing environment. In this scheme, we design an outsourced computing toolkit, which allows the users to achieve a series of secure integer and floating-point computing operations. During the implementation, LightPyFE can ensure that the users just perform the encryption and decryption operations, where all computing tasks are outsourced to edge servers for specific processing. Theoretical analysis and experimental results have demonstrated that our scheme can successfully achieve privacy-preserving feature extraction for EEG signals, and is practical yet effective.
Keyword ：
Additive secret sharing Additive secret sharing edge computing edge computing electroencephalogram (EEG) signal electroencephalogram (EEG) signal Internet of Things (IoT) Internet of Things (IoT) privacy-preserving privacy-preserving
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yan, Nazhao , Cheng, Hang , Liu, Ximeng et al. Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (2) : 2520-2533 . |
| MLA | Yan, Nazhao et al. "Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing" . | IEEE INTERNET OF THINGS JOURNAL 11 . 2 (2024) : 2520-2533 . |
| APA | Yan, Nazhao , Cheng, Hang , Liu, Ximeng , Chen, Fei , Wang, Meiqing . Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (2) , 2520-2533 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
由于细粒度图像类间差异小,类内差异大的特点,因此细粒度图像分类任务关键在于寻找类别间细微差异.最近,基于Vision Transformer的网络大多侧重挖掘图像最显著判别区域特征.这存在两个问题:首先,网络忽略从其他判别区域挖掘分类线索,容易混淆相似类别;其次,忽略了图像的结构关系,导致提取的类别特征不准确.为解决上述问题,本文提出动态自适应调制和结构关系学习两个模块,通过动态自适应调制模块迫使网络寻找多个判别区域,再利用结构关系学习模块构建判别区域间结构关系;最后利用图卷积网络融合语义信息和结构信息得出预测分类结果.所提出的方法在CUB-200-2011数据集和NA-Birds数据集上测试准确率分别达到92.9%和93.0%,优于现有最先进网络.
Keyword ：
Vision Transformer (ViT) Vision Transformer (ViT) 动态自适应调制 动态自适应调制 图卷积网络 图卷积网络 细粒度图像分类 细粒度图像分类 结构关系学习 结构关系学习
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 王衍根 , 陈飞 , 陈权 . 结合动态自适应调制和结构关系学习的细粒度图像分类 [J]. | 计算机系统应用 , 2024 , 33 (08) : 166-175 . |
| MLA | 王衍根 et al. "结合动态自适应调制和结构关系学习的细粒度图像分类" . | 计算机系统应用 33 . 08 (2024) : 166-175 . |
| APA | 王衍根 , 陈飞 , 陈权 . 结合动态自适应调制和结构关系学习的细粒度图像分类 . | 计算机系统应用 , 2024 , 33 (08) , 166-175 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
