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New Layered SOA-Based Architecture for Multi-Robots Cooperative Online SLAM

Chinese Journal of Electronics 2025年第1期23卷 25-30页

作者： Yunfei Cai Zhenmin Tang Chunxia Zhao Institute of Pattern Recognition and Artificial Intelligence Nanjing University of Science and Technology Nanjing China

A new layered multi-robots architecture for cooperative online simultaneous localization and mapping based on Service-oriented architecture (SOA) is proposed. This architecture is constructed by the concepts of service-oriented and loosely coupled center scheduling. These methods make robots' underlying function realization be transparent in cooperation and avoid the impact affected by robot heterogeneous characteristics, which will be beneficial to the system construction, expansion, restructuring and maintenance. All data packages follow the SOA unified data format, which can avoid the impact affected by sensor heterogeneous characteristics. The point to point serving model reduces the path of data transmission which can improve the timeliness of the system. All of these features can satisfy the requirements of multi-robots cooperative online SLAM. The simulation results show the feasibility, applicability and practicability of this architecture.

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EmoFake: An Initial Dataset for Emotion Fake Audio Detection 23rd

EmoFake: An Initial Dataset for Emotion Fake Audio Detectio...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Zhao, Yan Yi, Jiangyan Tao, Jianhua Wang, Chenglong Dong, Yongfeng School of Artificial Intelligence Hebei University of Technology Tianjin China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9789819783663

To enhance the effectiveness of fake audio detection techniques, researchers have developed multiple datasets such as those for the ASVspoof and ADD challenges. These datasets typically focus on capturing non-emotional characteristics in speech, such as the identity of the speaker and the authenticity of the content. However, they often overlook changes in the emotional state of the audio, which is another crucial dimension affecting the authenticity of speech. Therefore, this study reports our progress in developing such an emotion fake audio detection dataset involving changing emotion state of the origin audio named EmoFake. The audio samples in EmoFake are generated using open-source emotional voice conversion models, intended to simulate potential emotional tampering scenarios in real-world settings. We conducted a series of benchmark experiments on this dataset, and the results show that even advanced fake audio detection models trained on the ASVspoof 2019 LA dataset and the ADD 2022 track 3.2 dataset face challenges with EmoFake. The EmoFake is publicly available (https://***/records/10443769) now. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： emotion fake emotion voice conversion fake audio detection dataset

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Data driven analysis of particulate systems for development of reliable model to determine drag coefficient of non-spherical particles

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Particuology 2025年第2期97卷 219-235页

作者： Tauseeq Hussain Atta Ullah Rehan Zubair Khalid Farooq Ahmad Fei Li Asifullah Khan Department of Chemical Engineering Pakistan Institute of Engineering and Applied SciencesIslamabad45650Pakistan PIEAS Artificial Intelligence Center Pakistan Institute of Engineering and Applied SciencesNiloreIslamabad45650Pakistan Department of Chemical and Materials Engineering College of EngineeringNorthern Border UniversityArarSaudi Arabia Institute of Process Engineering Chinese Academy of SciencesBeijingChina Pattern Recognition Lab Department of Computer and Information SciencesPakistan Institute of Engineering and Applied SciencesNiloreIslamabad45650Pakistan

Non-spherical particles are extensively encountered in the process industry such as feedstock or catalysts e.g.,energy,food,pharmaceuticals,and *** design of equipment used to process these particles is highly dependent upon the accurate and reliable modeling of hydrodynamics of particulate media *** coefficient of these particles is the most significant of all parameters.A universal model to predict the drag coefficient of such particles has not yet been developed due to the diversity and complexity of particle shapes and *** this into consideration,we propose a unique approach to model the drag coefficient of non-spherical particles using machine learning(ML)to move towards generalization.A comprehensive database of approximately five thousand data points from reliable experiments and high-resolution simulations was compiled,covering a wide range of *** drag coefficient was modeled as a function of Reynolds number,sphericity,Corey Shape Factor,aspect ratio,volume fraction,and angle of *** ML techniques—artificial Neural Networks,Random Forest,and AdaBoost—were used to train the *** models demonstrated strong generalization when tested on unseen ***,AdaBoost outperformed the others with the lowest MAPE(20.1%)and MRD(0.069).Additional analysis on excluded data confirmed the robust predictive abilities and generalization of the proposed *** models were also evaluated across three flow regimes—Stokes,transitional,and turbulent—to further assess their generalization.A comparative analysis with well-known empirical correlations,such as Haider and Levenspiel and Chien,showed that all ML models outperformed traditional approaches,with AdaBoost achieving the best *** current work demonstrates that new generated ML techniques can be reliably used to predict drag coefficient of non-spherical particles paving way towards generalization of ML approach.

关键词： Drag coefficient Non-spherical Neural network Random forest AdaBoost

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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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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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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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Visible-Infrared Person Re-identification with Real-world Label Noise

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4857-4869页

作者： Zhang, Ruiheng Cao, Zhe Huang, Yan Yang, Shuo Xu, Lixin Xu, Min Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control School of Mechatronical Engineering Beijing100081 China Chinese Academy of Sciences New Laboratory of Pattern Recognition State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China University of Technology Sydney School of Electrical and Data Engineering Faculty of Engineering and Information Technology UltimoNSW2007 Australia

In recent years, growing needs for advanced security and traffic management have significantly heightened the prominence of the visible-infrared person re-identification community (VI-ReID), garnering considerable attention. A critical challenge in VI-ReID is the performance degradation attributable to label noise, an issue that becomes even more pronounced in cross-modal scenarios due to an increased likelihood of data confusion. While previous methods have achieved notable successes, they often overlook the complexities of instance-dependent and real-world noise, creating a disconnect from the practical applications of person re-identification. To bridge this gap, our research analyzes the primary sources of label noise in real-world settings, which include a) instantiated identities, b) blurry infrared images, and c) annotators' errors. In response to these challenges, we develop a Robust Hybrid Loss function (RHL) that enables targeted recognition and retrieval optimization through a more fine-grained division of the noisy dataset. The proposed method categorises data into three sets: clean, obviously noisy, and indistinguishably noisy, with bespoke loss calculations for each category. The identification loss is structured to address the varied nature of these sets specifically. For the retrieval sub-task, we utilize an enhanced triplet loss, adept at handling noisy correspondences. Furthermore, to empirically validate our method, we have re-annotated a real-world dataset, SYSU-Real. Our experiments on SYSU-MM01 and RegDB, conducted under various noise ratios of random and instance-dependent label noise, demonstrate the generalized robustness and effectiveness of our proposed approach. © 1991-2012 IEEE.

关键词： Information management

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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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Unveiling Deepfakes with Latent Diffusion Counterfactual Explanations

Unveiling Deepfakes with Latent Diffusion Counterfactual Exp...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chen Yang Bo Peng Jing Dong Xiaoyu Zhang Institute of Information Engineering Chinese Academy of Sciences School of Cyberspace Security University of Chinese Academy of Sciences New Laboratory of Pattern Recognition (NLPR) State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deepfake technology, driven by deep learning, produces highly convincing synthetic media, raising concerns about misuse. While DeepFake detection models have achieved impressive accuracy, but due to the difficulty of distinguishing fake from real, interpretability remains challenging that humans cannot understand or trust the detection results. We propose a novel approach to enhance interpretability by generating counterfactual explanations. By integrating ensemble classifier loss and text instructions into the fine-tuning of a Latent Diffusion Model, our method effectively improves the quality and efficiency of generated counterfactual explanations. Experiments on DeepFake datasets validate the effectiveness of our approach, contributing the interpretability of Deepfake detection.

关键词： Deep learning Deepfakes Accuracy Signal processing Media Diffusion models Acoustics Speech processing

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