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3rd International Conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Zeng, Yuxian Zhang, Xinxin College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781510689077

Deep learning techniques have significantly improved the accuracy and efficiency of change detection of very high resolution (VHR) images. However, many current models ignore the inherent heterogeneity of bi-temporal remote sensing images, thus making it difficult to distinguish between target changes and background variations. To address this problem, we propose a deep learning (DL) framework, namely temporal-spatial feature coordination network (TSFCN), to improve the accuracy of detection by enhancing the differentiation of change pixel features and improving the extraction and processing of change information. Further comparison and ablation experiments on the SVCD dataset show that the proposed model, compared with other models, improves the F1 scores and IoU by 2.09% and 3.81%, respectively. The results demonstrate that our model can achieve better accuracy and performance improvement. © 2025 SPIE.

关键词： Change detection

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Adjustable Gating Prompt Transformer for Facial Attribute recognition with Limited Labeled Data 27th

Adjustable Gating Prompt Transformer for Facial Attribute R...

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27th International Conference on pattern recognition, ICPR 2024

作者： Ye, Qinxian Chen, Si Wang, Da-Han Jiang, Nanfeng Su, Yanfei Yan, Yan Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Informatics Xiamen University Xiamen361005 China

ISBN: (纸本)9783031781032

Existing supervised facial attribute recognition (FAR) methods that rely on large labeled datasets can pose a challenge in real-world scenarios. In the case of limited labeled data, the current methods that introduce auxiliary tasks with a large number of parameters are not conducive to the embedded applications of FAR. To overcome these challenges, this paper develops an adjustable gating prompt Transformer that can handle the limited labeled FAR task with a small number of training parameters. Specifically, we employ an effective image-guided prompt tuning, where the image-related prompt sequence is first generated by feeding image tokens into an image-guided prompt generation network (IPG-Net). Then, the prompt sequence can learn facial image information and guide the frozen pre-trained Transformer to fine-tune the model. In addition, dynamically adjustable gating is applied to the prompt sequence to adaptively adjust the contribution of the prompts from different encoder layers, which enhances the interaction between the different encoder layers and retains effective feature information during the iterative process. Experimental results on the CelebA and LFWA datasets demonstrate that our method outperforms competitive methods with a very small amount of training parameters when only limited labeled data are used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal encoding

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Document image Shadow Removal via Frequency Information-Oriented Network 27th

Document Image Shadow Removal via Frequency Information-Ori...

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27th International Conference on pattern recognition, ICPR 2024

作者： Yang, Fan Zhou, Xinyue Jiang, Nanfeng Wang, Da-Han Zhang, Xu-Yao Li, Guantin Man, Wang Wu, Yun School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781186

Removing shadows from document images can significantly improve the Quality of Experience (QoE) and boost the performance of the downstream document analysis and recognition tasks. However, existing methods still have limited generalization ability on complex document images and are prone to disrupt the image details. To address this issue, we consider the different shadow types that impact the image content on different frequency sub-bands. This motivates us to exploit frequency-domain information and further design a Frequency Information-oriented Deshadow Network (FID-Net). The proposed FID-Net mainly uses two elaborated modules, named Frequency Feature Extractor (FFE) and a Frequency Feature Refinement (FFR). FFE can generate low/high-frequency features through adaptively decomposing spectra of the shadow image. After that, FFR further refines both frequency features with mutual information operations. With the proposed key designs, extensive experimental results on the commonly used benchmarks demonstrate that the proposed method can learn discriminative shadows and achieve favorable performance against state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image enhancement

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DocHFormer: Document image Dewarping via Harmonized Modeling of Hierarchical Priors 27th

DocHFormer: Document Image Dewarping via Harmonized Modelin...

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27th International Conference on pattern recognition, ICPR 2024

作者： Zhou, Xinyue Li, Guanting Jiang, Nanfeng Wang, Da-Han Zhang, Xu-Yao Zhu, ShunZhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781186

Document image Dewarping (DID) task aims to address the issue of geometry distortion and improve image quality. In this paper, we propose a simple but effective method, named DocHFormer, that can take hierarchical priors features of images, including document image mask and coordinate positions, as additional information to realize accurate representation. To better exploit these fused information for dewarping, we take them into a harmonized space random shuffle operation, which can stochastically rearrange the pixels across spatial space and further use inverse operation to recover the original order. This way can adapt to allocate each feature pixel with equal probability and thus make full use of multi-type features. Furthermore, we introduce this mechanism into local self-attention to use linear complexity to input resolution and also design a new feed-forward network with structural modeling to boost representation. With the help of the above components, our proposed DocHFormer can achieve competitive performance with lower complexity and also outperform the existing state-of-the-art on several popular datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image enhancement

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SANS: Spatial-Aware Neural Solver for Plane Geometry Problem 27th

SANS: Spatial-Aware Neural Solver for Plane Geometry Proble...

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27th International Conference on pattern recognition, ICPR 2024

作者： Lin, Zi-Hao Xiao, Shun-Xin Chen, Zi-Rong Li, Jian-Min Wang, Da-Han Zhang, Xu-Yao School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Geometry problem solving (GPS) is an important research direction in artificial intelligence. Previous studies have demonstrated the effectiveness of neural solvers in GPS. However, they are deficiencies in accurately representing spatial relationships of geometric primitives within visually rich geometric diagrams. This paper presents a novel neural solver termed spatial-aware neural solver (SANS) that can perceive spatial relationships between geometric primitives. SANS includes two new modules: multimodal dual-branch spatial awareness pre-trained language module and point-primitive spatial-aware attention module. The pre-training module employs a dual-branch visual-textual point-matching strategy to align visual and textual points, and utilizes semantic structure pre-training to model global relationships. Additionally, the point-primitive spatial awareness attention module enhances the model’s ability to perceive spatial relationships between geometric primitives by accounting for the relative positions of points. Experiments show that SANS achieves 81.5 and 74.1 of accuracy on the Geometry3K and PGPS9K datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Learning Explicit Radical Representations for Zero-Shot Chinese Character recognition 27th

Learning Explicit Radical Representations for Zero-Shot Chin...

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27th International Conference on pattern recognition, ICPR 2024

作者： Pan, Song-Liang Wang, Da-Han Jiang, Nanfeng Zhang, Xu-Yao Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Zero-shot Chinese character recognition (ZSCCR) aims to recognize unseen Chinese characters by learning the semantic knowledge of seen characters. Radical-based methods treat Chinese characters as combinations of radicals, recognizing characters by predicting the radicals in the images. Existing radical-based methods have a closed radical parsing process that cannot be intervened in mid-course, relying only on semantic labels for constraints. However, semantic embedding vectors are usually manually designed and lack alignment with visual features, making it extremely difficult for the model to learn and locate discriminative radical representations from visual features. This paper proposes a ZSCCR network called Learning Explicit Radical Representations (LERRNet). LERRNet introduces learnable attribute hint vectors to guide the model in locating discriminative radicals and learning explicit representations of images. Specifically, we introduce a Radical Relevance Enhanced Encoder (RREE) to enhance the correlation of local radicals by augmenting the relationships between grid regions in visual features. Guided by attribute hint vectors, LERRNet employs a Radical Representation Decoder (RRD) to locate the most relevant regions of each radical in the given image and learn explicit radical representations. Extensive experiments demonstrate that LERRNet outperforms state-of-the-art radical/stroke-based methods across three ZSCCR benchmarks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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OCR4HSV: A Multi-task Learning Approach for Handwritten Signature Verification 27th

OCR4HSV: A Multi-task Learning Approach for Handwritten Sig...

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27th International Conference on pattern recognition, ICPR 2024

作者： Lin, Chao-Qun Wang, Da-Han Su, Yan-Fei Ge, De-Wu Zhang, Xu-Yao School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Xiamen KEYTOP Communication Technology Co. Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Handwritten signature verification (HSV) models are notably recognized for their ability to discern whether a signature is forged in an offline document. Recently, HSV technology has made significant development. However, these methods have primarily focused on the shape features of the text image and overlooked the textual feature information inherent in the text itself, which makes the HSV model overfit. In this paper, we propose a novel network model named OCR4HSV to solve the above shortcomings. The proposed OCR4HSV first attempts to combine OCR and HSV to learn textual features in a multi-task learning manner. The model employs a dual-parameter approach, combining shared parameters and independent parameters. Specifically, Within the shared parameters, the Laplace attention module (LAM) is incorporated for edge information extraction. For independent parameters, CN-Mamba is utilized for sequence feature extraction in OCR, and the multi-scale global fusion block (MGFB) is designed to enhance the distinction between reference and test sample pairs. Leveraging OCR-related information and these architectural enhancements can fully mine the inherent textual feature information and significantly mitigate overfitting in the HSV task, thereby boosting verification accuracy. Our model has achieved state-of-the-art performance on the ChiSig and HanSig datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi-task learning

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AMST: Object tracking based on collaborative framework with adaptive multi-strategy

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Information Sciences 2025年 718卷

作者： Rui Xu Si Chen Yan Yan Da-Han Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen 361024 China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen 361005 China

Deep object tracking can be modeled by online learning to adapt to the appearance changes, or offline learning to achieve fast tracking speed. However, either online or offline learning trackers are still difficult to continuously cope with challenging tracking scenes, due to the cumulative errors easily caused by online learning trackers and the object appearance changes often neglected by offline learning trackers. To overcome this problem, we propose a novel object tracking method based on collaborative framework with adaptive multi-strategy, enabling automatic switch between the online and offline learning trackers. In this framework, we first design the variable action set (VAS) module to adaptively choose the different trackers, tracking strategies, and template strategies with multiple action labels. Moreover, we incorporate the template update and selection (TUS) module, which dynamically updates and selects templates from a memory unit to adapt to object appearance changes. To update the action set and the memory unit, we further devise the online reliability evaluation (ORE) module that not only evaluates the tracking result and the tracker itself, but also estimates the quality and quantity of templates. Comprehensive experiments on challenging short-term and long-term tracking benchmarks demonstrate the remarkable performance of the proposed method.

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MoAFormer: Aggregating Adjacent Window Features into Local Vision Transformer Using Overlapped Attention Mechanism for Volumetric Medical Segmentation 11

MoAFormer: Aggregating Adjacent Window Features into Local V...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Luo, Yixi Yin, Huayi Du, Xia Department of Computer and Information Engineering Fujian Provincial Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

The window-based attention is used to alleviate the problem of abrupt increase in computation as the input image resolution grows and shows excellent performance. However, the problem that aggregating global features from different windows is waiting to be resolved. Swin-Transformer is proposed to construct hierarchical encoding by a shifted-window mechanism to interactively learn the information between different windows. In this work, we investigate the outcome of applying an overlapped attention block (MoA) after the local attention layer and apply plenty to medical image segmentation tasks. The overlapped attention module employs slightly larger and overlapped patches in the key and value to enable neighbouring pixel information transmission, which leads to significant performance gain. The experimental results on the ACDC and Synapse datasets demonstrate that the used method performs better than previous Transformer models. © 2022 ACM.

关键词： image resolution

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Graph convolutional network combined with random walks and graph attention network for node classification

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The Journal of China Universities of Posts and Telecommunications 2024年第3期31卷 1-14页

作者： Chen Yong Xie Xiaozhu Weng Wei College of Computer and Information Engineering Xiamen University of TechnologyXiamen 361024China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of TechnologyXiamen 361024China

Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor ***,this weighting mechanism is learned based on static information,which means it is susceptible to noisy nodes and edges,resulting in significant *** this paper,a method is proposed to obtain context dynamically based on random walk,which allows the context-based weighting mechanism to better avoid noise ***,the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT)network to form a model based on a mixed weighting *** model is named as the context-based and content-based graph convolutional network(CCGCN).CCGCN can better discover important neighbors,eliminate noise edges,and learn node embedding by message *** show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.

关键词： graph neural network(GNN) graph convolutional network(GCN) semi-supervised classification graph analysis

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