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arXiv 2025年

作者： Wan, Xixi Zheng, Aihua Wang, Zi Jiang, Bo Tang, Jin Ma, Jixin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Biomedical Engineering Anhui Medical University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in the case of low-quality features. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Modal analysis

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Effect of Ti-Cr Filler on Microstructure, Mechanical Properties, and Thermal Shock Resistance of Tzm/Graphite Brazed Joints

SSRN

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SSRN 2023年

作者： Liu, Dong-Guang Zou, Jin-Xin Zhang, Peng Zhou, Si-Wei Liu, Hui Li, Ze-Qun Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment Industry & Equipment Technology Hefei University of Technology Hefei230009 China School of Materials Science and Engineering Hefei University of Technology Hefei230009 China Anhui YaLan Seal Component Co. Ltd Chuzhou233200 China Fujian Provincial Key Laboratory of Advanced Materials Processing and Application Fujian University of Technology Fuzhou350118 China Intelligent Manufacturing Institute Hefei University of Technology Hefei230009 China

One series of TZM/Ti-40Cr/graphite brazed joints were successfully obtained by connecting TZM alloy and graphite with Ti-40Cr filler through brazing in controlled atmosphere. The brazed joints' microstructure, mechanical properties, and thermal shock properties were characterized by X-ray diffraction, scanning electron microscopy, and a 3D laser confocal measuring microscope. The results indicated that the interface microstructure of TZM/graphite brazed joints was graphite/Cr3C2/TiC/Ti-Cr(Mo)/Ti-Mo(Cr)/TZM, and the thickness of the brazing seam increases with increasing temperature. In contrast, the thickness of the carbide reaction layer near the graphite side gradually becomes thicker. The shear tests showed that the joint's strength increased with the temperature increase in a specific temperature range. Maximum shear strength of 26.5 MPa was reached when the joint with groove structure brazed at 1480°C. The effect of holding time on the joints was similar to that of temperature, but the thickness of the carbide reaction layer did not change with holding time. The optimal brazing process for TZM/Ti-40Cr/graphite brazed joints was to hold for 20 min at 1480°C. TZM/Ti-40Cr/graphite brazed joints were subjected to a laser thermal shock test. The results indicated that, at most, TZM/Ti-40Cr/graphite brazed joints could only withstand laser thermal shock of 20A current. When the current rose to 40A, the TZM/Ti-40Cr/graphite joint was penetrated by laser, and the damage depth reached -253.67 μm and -254.824 μm. © 2023, The Authors. All rights reserved.

关键词： Brazing

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UJN-Traffic: A Benchmark Dataset for Performance Evaluation of Traffic Element Classification 7th

UJN-Traffic: A Benchmark Dataset for Performance Evaluation ...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Li, Yan Han, Shi-Yuan Shen, Yuan Liu, Zhen Dong, Ji-Wen Xu, Tao University of Jinan Jinan250022 China China High-Resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

The detection of traffic element in remote sensing imagesplays an important role in the construction of traffic infrastructure, and can provide dynamic monitoring and quality evaluation for traffic construction projects. The convolutional neural network (CNN)-based method has achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, the existing dataset can only be used for object detection, and the sample size and diversity need to be improved. These problems have brought challenges to the improvement and application of existing traffic element detection methods. This paper proposes a large-scale highresolution traffic element dataset based on multi-method detection analysis, and validates it based on the CNN-based object detection method. We screened more than 100 satellite images from high-resolution satellite images with a spatial resolution of 0.8 m. Manually labeled 5 categories and 100,000 traffic element samples. In the experiment, three CNN-based detection methods were used as the experimental baseline method. The experimental results proved effectiveness of the UJN-traffic traffic element dataset and provided a useful reference for traffic construction engineering and management. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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A Flexible Two-Stage Constrained Multi-Objective Evolutionary Algorithm Based on Autonomic Regulation

SSRN

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SSRN 2022年

作者： Zou, Juan Luo, Jian Liu, Yuan Yang, Shengxiang Zheng, Jinhua Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Computer science College of Xiangtan University Hunan Province Xiangtan China Faculty of Informational Engineering University of Xiangtan Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

The core element in solving constrained multi-objective problems (CMOPs) with evolutionary algorithms is simultaneously balancing objective optimization and constraint satisfaction. The di ffi culty of maintaining this balance increases significantly for existing algorithms when dealing with complex CMOPs because various complex feasible regions often make CMOPs with very di ff erent characteristics. To address this issue, we propose a more flexible two-stage evolutionary algorithm based on autonomic regulation (ARCMO), which can e ff ectively control evolutionary trends to adapt to complex CMOPs. Specifically, the first stage performs a fast global search and passes the population information to the second stage. The second stage consists of two dynamically complementary sub-processes: named exploration subprocess and convergence subprocess. The ratio of these two sub-processes is dynamically adjusted according to the information from the first stage to adapt to CMOPs with di ff erent complexities. Experiments on several recently proposed benchmark suites and real-world application problems show that ARCMO is more adaptable than the contender algorithms when solving di ff erent complex CMOPs. © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Challenge-aware U-net for breast lesion segmentation in ultrasound images

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Pattern Recognition 2025年 168卷

作者： Dengdi Sun Changxu Dong Yuchen Yan Bo Jiang Yayang Duan Zhengzheng Tu Chaoxue Zhang Key Laboratory of Intelligent Computing & Signal Processing (ICSP) Ministry of Education School of Artificial Intelligence Anhui University China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui Provincial Key Laboratory of Multimodal Cognitive Computing School of Computer Science and Technology Anhui University China Department of Ultrasound the First Affiliated Hospital of Anhui Medical University China

Deep learning methods can enhance the efficiency of tumor segmentation in breast ultrasound (BUS) images. However, noise interference, small tumors, and blurred boundaries can reduce segmentation accuracy. We design a three-branch challenge-aware U-net (CAU-net) to address these main challenges in BUS images. Our CAU-net extracts the features from three challenge-aware encoders in parallel first. Secondly, we propose an adaptive aggregation layer (AAL) to merge the multi-scale features of three challenging branches, enabling the network to adaptively handle different breast lesion samples with these main challenges. To further enhance the accuracy of segmentation, we introduce the graph reasoning module (GRM) to the network to model the correlation between the channels of the features and acquire the global information in the features. The result of our experiment on two datasets demonstrates the superiority of CAU-net over the advanced medical image segmentation methods. Our code can be downloaded from https://***/tzz-ahu .

关键词： Image segmentation

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UOD: Universal One-shot Detection of Anatomical Landmarks

arXiv

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arXiv 2023年

作者： Zhu, Heqin Quan, Quan Yao, Qingsong Liu, Zaiyi Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China

One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data. Moreover, one-shot learning is not robust that it faces performance drop when annotating a sub-optimal image. To tackle these issues, we resort to developing a domain-adaptive one-shot landmark detection framework for handling multi-domain medical images, named Universal One-shot Detection (UOD). UOD consists of two stages and two corresponding universal models which are designed as combinations of domain-specific modules and domain-shared modules. In the first stage, a domain-adaptive convolution model is self-supervised learned to generate pseudo landmark labels. In the second stage, we design a domain-adaptive transformer to eliminate domain preference and build the global context for multi-domain data. Even though only one annotated sample from each domain is available for training, the domain-shared modules help UOD aggregate all one-shot samples to detect more robust and accurate landmarks. We investigated both qualitatively and quantitatively the proposed UOD on three widely-used public X-ray datasets in different anatomical domains (i.e., head, hand, chest) and obtained state-of-the-art performances in each domain. The code is at https://***/heqin-zhu/UOD_universal_oneshot_detection. © 2023, CC BY-NC-SA.

关键词： Learning systems

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Multispectral Pansharpening Based on High-Pass Modulation Injection Model with Difference Factor

Multispectral Pansharpening Based on High-Pass Modulation In...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yiyang Su Hongyu Yao Peng Wang Xiwang Zhang Jue Wang College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application Ministry of Natural Resources Zhangzhou Institute of Surveying and Mapping Zhangzhou China Fujian Provincial Key Lab of Coastal Basin Environment Fujian Polytechnic Normal University Fuqing China Donghai Laboratory Zhoushan China Anhui Province Key Laboratory of Physical Geographic Environment Chuzhou University Chuzhou China Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions Ministry of Education Henan University Kaifeng China Rady School of Management University of California San Diego California USA

This paper proposes a multispectral pansharpening based on high-pass modulation injection model with difference factor (HPM-DF), which is dedicated to solving the problem of difference between multispectral image and panchromatic image acquired at different moments. In the high-pass modulation injection model, we introduce a difference factor and use the alternating direction method of multipliers (ADMM) to fully analyze the difference variability to derive the final fusion product. Experiments assessed at both reduced and full resolution show that the proposed method can acquire better performance than the traditional pansharpening methods.

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A Reconstruction Method for Cross-Cut Shredded Documents Based on the Extreme Learning Machine Algorithm

Research Square

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Research Square 2022年

作者： Zhang, Zhenghui Zou, Juan Zheng, Jinhua Yang, Shengxiang Gong, Dunwei Pei, Tingrui Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Information Engineering College of Xiangtan University Hunan Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom School of Information and Electronic Engineering China University of Mining and Technology Xuzhou221116 China

Reconstruction of cross-cut shredded text documents (RCCSTD) has important applications for information security and judicial evidence collection. The traditional method of manual construction is a very time-consuming task, so the use of computer-assisted efficient reconstruction is a crucial research topic. Fragment consensus information extraction and fragment pair compatibility measurement are two fundamental processes in RCCSTD. Due to the limitations of the existing classical methods of these two steps, only documents with specific structures or characteristics can be spliced, and pairing error is larger when the cutting is more fine-grained. In order to reconstruct the fragments more effectively, this paper improves the extraction method for consensus information and constructs a new global pairwise compatibility measurement model based on the extreme learning machine algorithm. The purpose of the algorithm’s design is to exploit all available information and computationally suggest matches to increase the algorithm’s ability to discriminate between data in various complex situations, then find the best neighbor of each fragment for splicing according to pairwise compatibility. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the matching accuracy of the proposed algorithm is better than that of the previously published classical algorithms and still ensures a higher matching accuracy in the noisy datasets, which can provide a feasible method for RCCSTD intelligent systems in real scenarios. © 2022, CC BY.

关键词： Knowledge acquisition

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Let's Play Music: Audio-Driven Performance Video Generation

Let's Play Music: Audio-Driven Performance Video Generation

引用

International Conference on Pattern Recognition

作者： Hao Zhu Yi Li Feixia Zhu Aihua Zheng Ran He Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multi-staged framework to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap provides more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and interframe temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework.

关键词： Instruments Transforms Synchronization Data mining Task analysis Space heating

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UJN-Land: A Large-Scale High-Resolution Parcel of Land of Multi-temporal Dataset with CNN Based Semantic Segmentation Analysis 7th

UJN-Land: A Large-Scale High-Resolution Parcel of Land of Mu...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Shen, Yuan Wang, Yulin Yang, Shiyong Li, Yan Han, Shiyuan Liu, Zhen Xu, Tao School of Information Science and Engineering University of Jinan Jinan250022 China Highresolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Shandong Institutes of Industrial Technonlogy Jinan250102 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

Semantic segmentation of land in remote sensing images plays an important role in urban management and rural planning, and can provide intelligent analysis for urban development. Convolutional neural network (CNN) based methods have achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, most of segmentation of land dataset were constructed based on various satellite images and samples that lack the number and diversity of existing datasets. These problems have brought challenges to the improvement and application of CNN based land parcel segmentation methods. This paper presented a large-scale high-resolution land dataset based on CNN analysis. More than 200,000 images of 256*256 pixels were obtained from Gaofen satellite images with a spatial resolution of 0.8 m, in which four category samples were manually annotated. In the experiment, three CNN based semantic segmentation methods were used as experimental baseline methods. The experimental results showed that the availability of the UJN-Land dataset is beneficial to the planning and management of cultivated land. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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