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

作者： Li, Cai-Xia Du, Xia Wu, Yun Wang, Da-Han Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Vehicle re-identification (Re-ID) aims to retrieve the target vehicle from a large dataset composed of vehicle images captured by multiple cameras. Most vehicles are difficult to recognize in the environment of low resolution, occlusion, and viewpoint change, which brings challenges to vehicle Re-ID. Existing work usually uses additional attribute information to distinguish different vehicles, such as color, viewpoint, and model. However, this requires expensive manual annotation. Therefore, we propose a three-branch network based on attention mechanism and local-global feature association (AM-LGFA) to improve the accuracy of vehicle Re-ID. In the global branch, the global features of the vehicle are extracted. A multi-scale channel attention module is introduced into the attention branch to suppress irrelevant information and extract important channel features. The features extracted from the backbone are divided into different stripe features in the horizontal direction in the local branch. Then connect each stripe feature with the global information to enhance the context between features. Finally, the features extracted from the three branches are concatenated as the feature representation of the test phase. The experimental results show that the features extracted by the AM-LGFA network are complementary. The effectiveness of this method is verified on two challenging public datasets, VehicleID and VeRi-776. © 2022 ACM.

关键词： Vehicles

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GCAT: graph calibration attention transformer for robust object tracking

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Neural Computing and Applications 2024年第23期36卷 14151-14172页

作者： Chen, Si Hu, Xinxin Wang, Da-Han Yan, Yan Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen361005 China

Recent Siamese trackers have taken advantage of transformers to achieve impressive advancements. However, existing transformer trackers ignore considering the positional and structural information between tokens, and traditional template update strategies easily introduce noises to the dynamic templates during tracking. In order to alleviate this issue, this paper develops a novel end-to-end graph calibration attention transformer network (GCAT) to enhance tracking robustness and accuracy. A graph calibration attention mechanism is first designed to calibrate and aggregate template information, for effectively updating dynamic templates during the tracking process. Specifically, each token is considered as a node in the graph, and then, we calculate the weight relationships between each node and their adjacent nodes. Thus, this mechanism can aggregate the global context information of the template and search nodes and activate feature channels based on weights and biases to obtain more discriminative feature information. Moreover, we leverage a multi-level dropout mechanism to perform the data dropout, the layer dropout, and the feature dropout on the data, network, and attention levels, respectively, to avoid overfitting of local-specific information and improve the generalization ability. Extensive experiments show the proposed method achieves superior performance on seven challenging benchmark datasets, i.e., OTB100, OTB2013, UAV123, LaSOT, GOT10K, VOT2020, and TrackingNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Aggregates

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A Novel Clustering Model TEC for Station Classification 37th

A Novel Clustering Model TEC for Station Classification

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Yuan, Lei Liu, Lijuan Zhu, Shunzhi Chen, Long College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9783031290558

Station classification plays an important role in urban intelligent transportation systems. Recently, most of studies usually choose different features to analyze the function of stations. Moreover, they usually choose K-means as the clustering method, which may lead to unstable clustering results. Therefore, a novel clustering model (Transformer Encoder Clustering, TEC) has been proposed for station classification by only using passenger flow data. Firstly, transformer encoder has been applied to deeply extract the features from original passenger flow. Then, an improved Canopy + has been proposed to determine the initial centers for K-means. Finally, a novel evaluation metric (NR) has been introduced to verify the stability and volatility of station classification results. Experiments are conducted on Xiamen bus rapid transit ridership dataset. The results show that the 44 stations have been classified into three categories, and the NRs in all stations are 0.75, which verifies the stability of results for station classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intelligent systems

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Multi-Head Attention Mechanism for MultiStation Passenger Flow Prediction

Multi-Head Attention Mechanism for MultiStation Passenger Fl...

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2022 International Symposium on Design Studies and Intelligence Engineering, DSIE 2022

作者： Chen, Long Liu, Lijuan Yuan, Lei College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781643683720

Passenger flow prediction is of great significance for public transportation. Most of the existing studies mainly predict the flow for a single station only extracting temporal features without considering spatial features. Passenger flow predicting for multiple stations or even the whole network is beneficial to grasp the overall situation, which has more research value in practical application. Thus, a passenger flow prediction model for multiple stations has been proposed based on spatio-Temporal attention mechanism. This model is applied to Xiamen bus rapid transit (BRT) using the time granularity of 5-min. The experimental results demonstrate that our model improves the prediction accuracy compared to the baselines. © 2023 The authors and IOS Press.

关键词： Forecasting

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Twofold Siamese Network with Attentional Feature Fusion for Object Tracking 22

Twofold Siamese Network with Attentional Feature Fusion for ...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Huang, Huang Chen, Si Wang, Da-Han Xu, Huarong School of Computer and Information Engineering Xiamen University of Technology China Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450396110

Object tracking is still a critical and challenging problem in computer vision. More and more researchers pay attention to applying deep learning to obtain the powerful feature for robust tracking. Nowadays, feature fusion is an essential part of Siamese tracking architectures. However, the existing feature fusion methods usually provide a fixed linear aggregation of feature maps, and this combination may not be appropriate for a specific object. In this paper, a twofold Siamese network, named SD-Siam, is proposed to extract the features of the object effectively. The template branch and the search branch are both composed of a deep layer sub-network and a shallow layer sub-network, which is used for feature fusion of the different network layers. Moreover, an attentional feature fusion scheme is employed to better fuse scale-inconsistent features, where a multi-scale channel attention module is used to fuse different scales of features. In addition, we respectively evaluate similarity measures for the features of deep layer sub-networks and the fused features of the template branch and the search branch, and then these two similarity response maps are added to obtain the tracking result. Experiments show the proposed SD-Siam outperforms representative trackers on several challenging benchmarks. © 2022 ACM.

关键词： Network layers

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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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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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A Dynamic Spatio-temporal Network with Self-attention for Multi-station Passenger Flow Prediction 12

A Dynamic Spatio-temporal Network with Self-attention for Mu...

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12th International Conference on Awareness Science and Technology, iCAST 2023

作者： Wang, Fengzhi Lv, Qinzhi Liu, Lijuan College of Computer and Information Engineering Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9798350324693

Passenger flow prediction is vitally significant for intelligent transportation systems (ITS). Most of the studies typically focus on the passenger flow prediction for an individual station, and only capture the temporal features without considering any spatial features. Constructing a passenger flow prediction model for multiple stations, or even a whole network, is more valuable for practical applications. Therefore, we develop a dynamic spatio-temporal network (DSTNet) with a self-attention (SA) mechanism for multi-station passenger flow prediction. A dynamic graph convolutional network (DGCN) is applied for the spatial feature extraction, and gated recurrent unit (GRU) is combined to learn the temporal features. SA is applied to further assign the weights for the extracted spatio-temporal features. The Experiment has been conducted on the passenger flow in the Xiamen bus rapid transit (BRT). The results demonstrate that the proposed DSTNet with SA (SA-DSTNet) outperforms the baselines in the multi-station passenger flow prediction task. © 2023 IEEE.

关键词： Forecasting

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