Published in International Conference on Document Analysis and Recognition, 2015
A method using SIFT and CDF for text - independent writer identification is proposed. It has two - stage processing and outperforms other algorithms on two datasets.
Recommended citation:
Text-independent writer identification using SIFT descriptor and contour-directional feature, Y.-J. Xiong, Y. Wen, Patrick. S. P. Wang and Y. Lu*, in Proceedings of the International Conference on Document Analysis and Recognition, (2015) pp. 91–95
The paper proposes a method combining SIFT keypoint location and two - directional DTW for Chinese handwritten character detection without segmentation. It shows good results but has limitations.
Published in International Journal of Pattern Recognition and Artificial Intelligence, 2017
This paper focuses on off - line text - independent writer recognition. It summarizes methods, shows datasets, and compares performances. Spatial features outperform others in some aspects.
Recommended citation:
Off-line Text-independent Writer Recognition: A Survey, Y.-J. Xiong, Y. Lu* and Patrick. S. P. Wang, International Journal of Pattern Recognition and Artificial Intelligence, 2017, 31 (5): 1756008
Published in Series on Language Processing, Pattern Recognition, and Intelligent Systems, 2017
A method using CDF and modified SIFT for Chinese writer identification is proposed. It outperforms others, achieving high accuracy on HIT - MW dataset.
Recommended citation:
Chapter 8:Off-line Text-independent Writer Identification for Chinese Handwriting, Y.-J. Xiong and Y. Lu*, Advances in Chinese document and text processing, Series on Language Processing, Pattern Recognition, and Intelligent Systems, 2017, 2 (8): 215-234
Published in International Conference on Document Analysis and Recognition, 2017
A method using CDF and CPSM for Chinese writer identification fuses two similarities. It outperforms previous methods with high Top - 1 accuracy on two datasets.
Recommended citation:
Chinese Writer Identification Using Contour-Directional Feature and Character Pair Similarity Measurement, Y.-J. Xiong and Y. Lu*, in Proceedings of the International Conference on Document Analysis and Recognition, (2017) pp. 119–124
Published in International Journal of Pattern Recognition and Artificial Intelligence, 2019
This paper improves text - independent Chinese writer identification by using the similarity of character pairs. It proposes ECF - based scheme and DFS, and re - ranks candidates. Evaluated on two datasets, it outperforms existing methods with high Top - 1 accuracy.
Recommended citation:
Improving Text-Independent Chinese Writer Identification with the Aid of Character Pairs, Y.-J. Xiong, L. Liu, S.-J. Lyu, Patrick S. P. Wang and Y. Lu*, International Journal of Pattern Recognition and Artificial Intelligence, 2019, 33 (2): 1953001
Published in Frontiers in Patten Recognition and Artificial Intelligence, 2019
A method for Chinese writer identification uses text - independent and text - dependent features. It fuses two similarities. Experiments show it outperforms previous methods with high Top - 1 accuracy.
Recommended citation:
Chapter 6:Improving Chinese Writer Identification by Fusion of Text-dependent and Text-independent Methods, Y.-J. Xiong, L. Liu, Patrick S. P. Wang and Y. Lu*, Frontiers in Patten Recognition and Artificial Intelligence, Series on Language Processing, Pattern Recognition, and Intelligent Systems, 2019, 5 (6): 97-112
Published in Wuhan University Journal of Natural Sciences, 2020
A lightweight IU - Net with shallow features combination is proposed, reducing drawbacks, and is applied to detect small metal product defects, outperforming other methods.
Recommended citation:
A Lightweight Improved U-Net with Shallow Features Combination and Its Application to Defect Detection H. Wu, X.-K. Sun*, Y.-J. Xiong, Wuhan University Journal of Natural Sciences, 2020, 25 (5): 461-468
Published in International Conference on Pattern Recognition and Artificial Intelligence, 2020
The paper presents two CNN - based methods, one using CTPN for handwriting detection and the other using Mask - RCNN for hand - sketched graphics detection, and validates their effectiveness on the SUES - 1000 database.
Recommended citation:
Handwriting and Hand-Sketched Graphics Detection Using Convolutional Neural Networks, S.-Y. Cheng, Y.-J. Xiong*, J.-Q. Zhang and Y.-C. Cao, in Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence, (2020) pp. 352-362
The paper proposes an improved genetic algorithm - based path planning method for UAVs to ensure flight safety and shorter distances, and verifies its superiority through experiments.
Published in Wuhan University Journal of Natural Sciences, 2021
The paper presents an AUMF for license plate detection. It details its architecture, loss function, validates on AOLP dataset, and shows better performance in complex conditions.
Recommended citation:
Attention U-Net with Multilevel Fusion for License Plate Detection, Y. Yao, Y.-J. Xiong*, B. Huang and J. Yang, Wuhan University Journal of Natural Sciences, 2021, 26 (3): 227-234
Published in Digital TV and Wireless Multimedia Communication, 2021
This paper empirically examines the effects of text factors on Chinese writer identification with text - independent features. It concludes that more characters boost performance, 50 is the minimum number needed, and the number of same characters has little impact above 50.
Recommended citation:
An Empirical Study of Text Factors and Their Effects on Chinese Writer Identification, Y.-J. Xiong*, Y. Lu and Y.-C. Cao, Digital TV and Wireless Multimedia Communication, (2021) pp. 194-205
This paper proposes a method combining inverted feature pyramid and U - Net for hyperspectral image classification. It uses PCA for preprocessing, and experiments show high accuracy and analyze related factors.
Published in Journal of Circuits, Systems and Computers, 2021
U-Net is good at medical image segmentation but not for industrial defect detection. We propose an attention U-Net with a feature fusion module. It combines features and uses attention gates. Experiments on two datasets show it outperforms other methods and has application potential.
Recommended citation:
Attention U-Net with Feature Fusion Module for Robust Defect Detection, Y.-J. Xiong*, Y.-B. Gao, H. Wu and Y. Yao, Journal of Circuits, Systems and Computers, 2021, 30 (15): 2150272
We propose PC-SuperPoint using pyramid convolution and circle loss for interest point tasks. Pyramid convolutions extract multiscale features, circle loss aids training, and experiments on relevant datasets show its effectiveness.
Recommended citation:
PC-SuperPoint: interest point detection and descriptor extraction using pyramid convolution and circle loss, Y.-J. Xiong*, S. Ma, Y.-B. Gao and Z.-J. Fang, Journal of Electronic Imaging, 2021, 30 (3): 033024
Published in International Conference on Document Analysis and Recognition, 2021
The paper presents an attention - based Multiple Siamese Network for offline signature verification. It uses attention modules and contrastive pairs, and shows better performance than previous methods on multiple datasets.
Recommended citation:
Attention Based Multiple Siamese Network for Offline Signature Verification, Y.-J. Xiong* and S.-Y. Cheng, in Proceedings of the International Conference on Document Analysis and Recognition, (2021) pp. 337-349
Published in Expert Systems With Applications, 2022
The paper presents GMGSVMs and GMIGSVMs for generalized multi - view learning, uses an alternating algorithm for optimization, and proves their superiority via experiments.
Recommended citation:
Generalized multi-view learning based on generalized eigenvalues proximal support vector machines, X.-J. Xie* and Y.-J. Xiong, Expert Systems with Applications, 2022, 194 (1): 116491
The paper presents a knowledge - distilled pre - training model for VLN. It shrinks model size and inference time, keeps 95% of the original performance, and outperforms baselines.
Recommended citation:
Knowledge distilled pre-training model for vision-language-navigation, B. Huang*, S. Zhang, J.-T. Huang, Y.-J. Yu, Z.-C. Shi and Y.-J. Xiong, Applied Intelligence, 2022, 53 (1): 5607–5619
The paper presents a lightweight DS - PWC model based on PWC - Net for optical flow estimation. It uses deep - separable convolutions and data enhancement, achieving 58 fps with good quality and validating its effectiveness.
The paper proposes a cross - entropy approach to solve it, and validates its effectiveness via experiments.
Recommended citation:
A cross entropy based approach to minimum propagation latency for controller placement in Software Defined Network, J. Chen, Y.-J. Xiong*, X.-H. Qiu, D. He, H.-M. Yin and Y.-F. Xiao, Computer Communications, 2022, 191 (1): 133-144
The paper proposes an attention - guided dual feature pyramid network - based license plate detection method for complex environments, validates it on datasets, and proves its superiority.
Recommended citation:
License Plate Detection with Attention-Guided Dual Feature Pyramid Networks in Complex Environments, Y.-J. Xiong*, Y.-B. Gao*, J.-Q. Zhang and J.-X. Ren, Electronics, 2022, 11 (23): 3895
Published in International Conference on Cyber, 2022
The paper presents an improved DCPA for CPP. It calculates controller numbers, uses multiple indicators and K - means, and experiments show it gets low - cost solutions close to the optimal.
Recommended citation:
A Density-based Controller Placement Algorithm for Software Defined Networks, J. Chen, Y.-J. Xiong* and D. He, in Proceedings of the International Conference on Cyber, Physical and Social Computing, (2022) pp. 287-291
Published in International Conference on Ubiquitous Intelligence and Computing, 2022
The paper presents a SET for offline signature verification, using a modified Swin - Transformer and SE module, and it outperforms existing methods on multiple datasets.
Recommended citation:
SET: A squeeze-and-excitation transformer for offline signature verification, J.-X. Ren, J. Chen* and Y.-J. Xiong*,in Proceedings of the International Conference on Ubiquitous Intelligence and Computing, (2022) pp. 1812-1816
This paper presents a vertebral corner detection framework with an embedded Transformer mechanism for calculating Cobb angles. It uses data augmentation, Transformer, and Vector Loss modules to solve automated measurement issues. Experiments on the MICCAI 2019 dataset show the method has high accuracy (SMAPE of 9.01, 1.80 improvement), and can help clinical decision - making. Future work will focus on reducing model depth and complexity.
Published in Journal of Circuits, Systems, and Computers, 2022
In the big data era, conventional RWS in cloud computing has issues. We propose a new methodology. Simulations show our RWS strategies are superior and the method has potential for big data systems.
Recommended citation:
Mitigating Lifetime-Energy-Makespan Issues in Reliability-Aware Workflow Scheduling for Big Data, Y.-J. Xiong*, S.-Y. Cheng and B. Chen, Journal of Circuits, Systems and Computers, 2022, 31 (1): 2250012
Deep learning in computer vision is limited by data - scale dependence. This paper uses Swin Transformer with fine - tuning to overcome data shortage, showing good small - scale dataset object - recognition performance.
Recommended citation:
Learning Transferable Feature Representation with Swin Transformer for Object Recognition, J.-X. Ren, Y.-J. Xiong*, X.-J. Xie and Y.-F. Dai, Neural Processing Letters, 2023, 55 (1): 2211–2223
The paper “License Plate Detection Using Siamese Feature Pyramid and Cascaded Positioning” presents an algorithm with a Siamese feature pyramid and cascaded positioning. It performs better than traditional methods on relevant datasets.
Published in International Journal on Document Analysis and Recognition, 2023
The paper “Attention-based multiple siamese networks with primary representation guiding for offline signature verification” presents a method with siamese networks and special modules. It outperforms others on multiple datasets in offline signature verification.
Recommended citation:
Attention-based multiple siamese networks with primary representation guiding for offline signature verification, Y.-J. Xiong*, S.-Y. Cheng, J.-X. Ren and Y.-J. Zhang, International Journal on Document Analysis and Recognition,2024,195–208
This paper proposes a biomedical event trigger detection method based on a two - stage question - answering paradigm. It uses syntactic - distance - based attention and word - entity - event co - occurrence features to address existing problems in trigger detection. Experiments on the MLEE corpus show that the model outperforms baseline models, with an F1 - score of 81.39%, and the author also plans to explore further improvements in the future.
Published in Expert Systems With Applications, 2023
“PDCSN: A partition density clustering with self - adaptive neighborhoods” presents PDCSN. It uses self - adaptive methods to cluster, and outperforms rivals on multiple datasets.
Recommended citation:
PDCSN: A partition density clustering with self-adaptive neighborhoods, S. Xing, Q.-M. Su*, Y.-J. Xiong*, C.-M. Xia,Expert Systems With Applications, 2023, 227 (1): 120195
This paper constructs the CODP - 1200 dataset using AIGC for child language acquisition and proposes the DDMXCap method, with experiments validating its efficacy.
Recommended citation:
CODP-1200: An AIGC based benchmark for assisting in child language acquisition, G. Leng, G. Zhang, Y.-J. Xiong* and J. Chen, Displays, 2023, 82: 102627
Published in Engineering Applications of Artificial Intelligence, 2023
The paper “2C2S: A two-channel and two-stream transformer based framework for offline signature verification” presents the 2C2S framework. It leverages a two - stream setup and special modules, outperforming rivals in signature verification.
Recommended citation:
2C2S: A two-channel and two-stream transformer based framework for offline signature verification, J.-X. Ren, Y.-J. Xiong*, H. Zhan and B. Huang, Engineering Applications of Artificial Intelligence, 2023, 118 (1): 105639
Published in IET Cyber-Physical Systems: Theory & Applications, 2023
This paper presents an MDR - GCN relation extraction model using multiple dependency tree representations and a GSF Extractor module, achieving good results on multiple datasets and analyzing relevant factors.
Recommended citation:
Multiple dependence representation of attention graph convolutional network relation extraction model, L.-F. Zhao, Y.-J. Xiong*, Y.-B. Gao and W.-J. Yu, IET Cyber-Physical Systems: Theory & Applications, 2023
Published in International Conference on Parallel and Distributed Systems, 2023
The paper “Deep Frame-Point Sequence Consistent Network for Handwriting Trajectory Recovery” presents a two - stream framework for handwriting trajectory recovery. It uses a module to synchronize training and shows good results in experiments.
Recommended citation:
Deep Frame-Point Sequence Consistent Network for Handwriting Trajectory Recovery, Y.-J. Xiong, Y.-F. Dai and D. Meng*, in Proceedings of the International Conference on Parallel and Distributed Systems, 2023,2151-2158
The paper proposes the Chain-of-LoRA framework, which trains a task - selection LoRA to classify instruction types and task - specific LoRAs for tasks. Experiments show it can achieve performance comparable to direct instruction fine - tuning, balancing performance and disk storage for resource - constrained users.
Recommended citation:
Chain-of-LoRA: Enhancing the Instruction Fine-Tuning Performance of Low-Rank Adaptation on Diverse Instruction Set,Qiu Xihe and Hao Teqi and Shi Shaojie and Tan Xiaoyu* and Xiong Yu-Jie,IEEE Signal Processing Letters,2024,31,875-879.
The paper proposes DDM - RLIP, which applies a discrete diffusion model with refined pre - trained representations to remote sensing image captioning. Experiments on three datasets show it outperforms traditional autoregressive models.
Published in Asian Conference on Computer Vision, 2024
The paper proposes the FaRE strategy, which incorporates visual feature clues into radical encodings. Experiments on ICDAR2013 show it improves zero - shot Chinese character recognition performance compared to state - of - the - art methods.
Recommended citation:
FaRE: A Feature-aware Radical Encoding Strategy for Zero-shot Chinese Character Recognition, Zhan Hongjian and Li Yangfu and Xiong Yu-jie and Lu Yue,Proceedings of the Asian Conference on Computer Vision (ACCV),2024,390-401.
In the field of tongue image segmentation, researchers have proposed a novel Transformer-based feature channel fusion method, which significantly improves segmentation accuracy and reliability.
This paper uses the frequent pattern mining algorithm and cross - merging method to establish TCM single - and multi - system symptom questioning strategies, which can improve the efficiency of obtaining patient symptom information and promote the objective development of TCM consultation.
The paper proposes LoRA², which trains LoRAs on orthogonal planes, improves the importance score algorithm, and shows better performance than baselines in fine - tuning large language models with fewer parameters.
Recommended citation:
LoRA² :Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models, J.-C. Zhang, Y.-J. Xiong*, X.-H. Qiu, D.-H. Zhu, C.-M. Xia, arxiv preprint, arxiv:2408.06854 (2024)
Published in ACM International Conference on Multimedia, 2024
This paper presents a frame - level contrastive learning framework with a Text Perceiver for lightweight scene text recognition models, improving performance, especially in low - quality scenarios, with effectiveness verified by experiments.
Recommended citation:
Free Lunch: Frame-level Contrastive Learning with Text Perceiver for Robust Scene Text Recognition in Lightweight Models, H.-J. Zhan, Y.-F. Li*, Y.-J. Xiong, Umapada Pal, Y. Lu, Proceedings of the 32nd ACM International Conference on Multimedia, 2024
Published in International Conference on MultiMedia Modeling, 2024
“LRATNet: Local-Relationship-Aware Transformer Network for Table Structure Recognition” presents LRATNet. It combines modules for local and global info, a new loss function, and outperforms rivals on 3 datasets in table structure recognition.
Recommended citation:
LRATNet: Local-Relationship-Aware Transformer Network for Table Structure Recognition, G. Yang, D. Zhong, Y.-J. Xiong and H. Zhan*, in Proceedings of the International Conference on MultiMedia Modeling (MMM), Lecture Notes in Computer Science, vol 14555, (2024)pp. 441-452
The paper proposes a text classification model with GATs and adversarial training, performs well in experiments, and discusses its limitations and future directions.
Recommended citation:
Text Classification Model Based on Graph Attention Networks and Adversarial Training, J. Li, Y. Jian* and Y.-J. Xiong, Applied Sciences, 2024, 14(11): 4906
The paper proposes MvHGLpLSTSVM for multi - view semi - supervised learning, combining hypergraph and Lp norm, and validates its effectiveness via experiments.
Recommended citation:
Multi-view hypergraph regularized Lp norm least squares twin support vector machines for semi-supervised learning, J. Lu, X.-J. Xie* and Y.-J. Xiong, Pattern Recognition, 2024, 156: 110753
Published in Expert Systems With Applications, 2024
The paper proposes a method using multiple Riemannian manifold - valued descriptors and audio - visual information for video clustering, including single - and multi - modality approaches, and shows its superiority over existing methods through experiments.
Recommended citation:
Enhanced video clustering using multiple riemannian manifold-valued descriptors and audio-visual information, W. Hu, H. Zhan*, Y. Tian, Y.-J. Xiong and Y. Lu, Expert Systems with Applications, 2024, 246: 123099
The paper proposes a Transformer - based end - to - end method with triplet deep attention to attack text CAPTCHAs, achieving high accuracy on Roman and Chinese captcha datasets and exploring its performance under various conditions.
Recommended citation:
Transformer-based end-to-end attack on text CAPTCHAs with triplet deep attention, B. Zhang, Y.-J. Xiong*, C.-M. Xia and Y.-B. Gao, Computers & Security, 2024, 146: 104058
The paper proposes the REMALIS framework for multi - agent coordination with LLMs. It uses intention propagation, bidirectional feedback, and recursive reasoning, outperforming baselines.
Recommended citation:
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models, X.-H. Qiu*, H.-Y. Wang, X.-Y. Tan, C. Qu, Y.-J. Xiong, Y. Chen, Y.-H. Xu, W. Chu, Y. Qi, arxiv preprint, arxiv:2407.12532 (2024)
Published in International Conference on Intelligent Technology and Embedded Systems, 2024
The paper proposes PointABM, which combines Bidirectional Mamba and Transformer, and it shows improved performance in point cloud analysis with a small increase in parameters.
Recommended citation:
PointABM: Integrating Bidirectional Mamba and Multi-Head Self-Attention for Point Cloud Analysis, J.-W. Chen, Y.-J. Xiong*, D.-H. Zhu, J.-C. Zhang, Z. Zhou, 2024 4th International Conference on Intelligent Technology and Embedded Systems (ICITES). IEEE, 2024
The paper “Kalman-SSM: Modeling Long-Term Time Series With Kalman Filter Structured State Spaces” presents the Kalman-SSM model. It combines the Kalman filter and SSM, outperforming SOTA models in long - term time series forecasting.
Recommended citation:
Kalman-SSM: Modeling Long-Term Time Series With Kalman Filter Structured State Spaces, Z. Zhou, X. Guo, Y.-J. Xiong* and C.-M. Xia, IEEE Signal Processing Letters, 2024, 31: 2470-2474
Published in Biomedical Signal Processing and Control, 2024
This paper proposes a lightweight medical image segmentation model named UConvNeXt based on depth - wise separable convolution and MLP. By using large - scale kernel depth - wise separable convolution and the local feature weighted fusion MLP (LFWF - MLP) module, experiments are carried out on multiple medical image datasets. The results show that while reducing parameters and computational complexity, the model can achieve comparable or even better segmentation performance than high - parameter models. Additionally, the limitations of the model and its future improvement directions are analyzed.
Recommended citation:
Harmonious parameters and performance: Lightweight convolutional stage and local feature weighted fusion MLP for medical image segmentation, Y.-X. Chen, Y.-J. Xiong*, X.-H. Qiu and C.-M. Xia*, Biomedical Signal Processing and Control, 2024, 98: 106726
Published in Engineering Applications of Artificial Intelligence, 2024
This paper presents AGFN for FER to handle data uncertainties. It uses a Poisson graph generator and GCN, and outperforms other methods, especially with mislabeled data.
Recommended citation:
Adaptive graph-based feature normalization for facial expression recognition, Y.-J. Xiong*, Q. Wang, Y.-T. Du and Y. Lu, Engineering Applications of Artificial Intelligence, 2024, 129: 107623
The paper proposes AutoGRN, which integrates an adaptive multi - channel framework and copula - based modeling for spatio - temporal fusion in power systems, outperforming benchmarks in multivariate prediction tasks.
Recommended citation:
AutoGRN: An adaptive multi-channel graph recurrent joint optimization network with Copula-based dependency modeling for spatio-temporal fusion in electrical power systems, H.-Y. Wang, X.-H. Qiu*, Y.-J. Xiong, X.-Y. Tan, Information Fusion, 2024: 102836
Published in Wuhan University Journal of Natural Sciences, 2024
A two - stage framework for few - shot NER is proposed. It uses multiscale convolution and an improved prototypical network, and outperforms baselines in experiments.
Recommended citation:
Few-Shot Named Entity Recognition with the Integration of Spatial Features, Z.-W. Liu, B. Huang*, C.-M. Xia, Y.-J. Xiong, Z.-S. Zhang, Y.-Q. Zhang, Wuhan University Journal of Natural Sciences, 2024,29.2: 125-133
Published in Engineering Applications of Artificial Intelligence, 2025
This paper presents TTMNM, a triple - strategy - based model, to validate knowledge graph triplets. Experiments show it outperforms baselines and is applicable in industrial datasets.
Recommended citation:
Triplet trustworthiness validation with knowledge graph reasoning, G. Zhang, Y.-J. Xiong*, J.-P. Hu, C.-M. Xia, Engineering Applications of Artificial Intelligence, 2025,146: 109813
This paper proposes the Iterative Summarization Pre-Prompting (ISP²) method, which enhances the complex reasoning capabilities of large language models by adaptively extracting candidate information, rating the reliability of information pairs, and performing iterative summarization. Experiments show that this method can significantly improve model performance. Additionally, the paper analyzes the summarization steps and error sources of ISP².
Published in International Conference on Computational Linguistics, 2025
This paper proposes DCFT, a method for parameter - efficient fine - tuning of large language models using deconvolution in subspace. It overcomes rank - one decomposition limitations, shows good performance with fewer parameters, and optimizes computational efficiency.
Recommended citation:
Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace, J.-C. Zhang, Y.-J. Xiong*, C.-M. Xia, D.-H. Zhu, X.-H. Qiu, Proceedings of the 31st International Conference on Computational Linguistics, 2025: 3924-3935