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学者姓名：钟尚平
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Currently, fabric image retrieval faces challenges such as the high cost of image annotation and its vulnerability to adversarial perturbations. To minimize manual supervision and enhance the robustness of the retrieval system, this study proposes a robust deep image retrieval algorithm using multi-view self-supervised product quantization for artificially generated fabric images. The method introduces a multi-view module, which includes two views enhanced by AutoAugment, an adversarial view and a high-frequency view of the unlabeled images. AutoAugment can generate more varied data variations, which allows the model to learn more about the different features and structures of the fabric texture; fabric images are usually of high complexity and diversity, and adding the adversarial sample into the model training can add more noise and variations, which is one of the best existing ways to defend against adversarial attacks; the high-frequency component can make the edges, details, and contrasts in the fabric image clearer. A robust cross quantized contrastive loss function is also designed to jointly learn codewords and deep visual descriptors by comparing multiple views, effectively increasing the model’s robustness and generalization. The method's effectiveness is demonstrated by experimental results on multiple datasets, which can significantly improve the robustness of the retrieval system compared to other state-of-the-art retrieval algorithms. Our method presents a new approach for fabric image retrieval and has great significance for improving its performance. © 2024 SPIE. All rights reserved.
Keyword ：
Image compression Image compression Image enhancement Image enhancement Image retrieval Image retrieval Search engines Search engines Textures Textures
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| GB/T 7714 | Zhuo, Yudan , Zhong, Shangping , Chen, Kaizhi . Multi-view Self-supervised Product Quantization for Robust Fabric Image Retrieval [C] . 2024 . |
| MLA | Zhuo, Yudan 等. "Multi-view Self-supervised Product Quantization for Robust Fabric Image Retrieval" . (2024) . |
| APA | Zhuo, Yudan , Zhong, Shangping , Chen, Kaizhi . Multi-view Self-supervised Product Quantization for Robust Fabric Image Retrieval . (2024) . |
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Lace texture, as a manually designed texture image, needs to possess a series of essential aesthetic characteristics, such as periodicity, symmetry, and blank-leaving in artistic design creation. It requires human designers to spend a lot of time and effort, so it is necessary to apply generative models to generate lace images. In image generation tasks, compared to models such as DCGAN, CycleGAN, and ProGAN, although images generated using StyleGAN2 perform well in terms of resolution, texture details, and periodicity, they still perform poorly in terms of symmetry in lace images. To address the above issues, this article proposes an improved model SStyleGAN (Symmetry StyleGAN) based on StyleGAN2. In terms of discriminators, in order to enhance the attention of the proposed model to image symmetry, we have added a symmetry discriminator, that is, SStyleGAN adopts a dual discriminator structure; In terms of generator, in order to improve the similarity of the feature maps on the left and right sides of the lace image, this paper adds a mean square error loss term based on the loss function of StyleGAN2; In terms of noise input, in order to control the symmetry of the lace image at details such as lace edges, the noise of the StyleGAN2 model is modified to a symmetrical structure, so that the noise input itself has symmetry. In addition to the commonly used FID (Fréchet Insertion Distance) in the generative model, we also used the SSIM (Structural Similarity) metric for the evaluation of the experimental results in this article to detect the symmetry of the generated images. The experimental results show that compared to the lace images generated by the StyleGAN2 model, the lace images generated by the model proposed in this paper not only inherit the advantages of the former, but also have symmetry characteristics. © 2024 SPIE.
Keyword ：
Discriminators Discriminators Generative adversarial networks Generative adversarial networks Image enhancement Image enhancement Image texture Image texture Mean square error Mean square error Textures Textures
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| GB/T 7714 | Li, Jian , Zhong, Shangping , Chen, Kaizhi . SStyleGAN: a StyleGAN model for generating symmetrical lace images [C] . 2024 . |
| MLA | Li, Jian 等. "SStyleGAN: a StyleGAN model for generating symmetrical lace images" . (2024) . |
| APA | Li, Jian , Zhong, Shangping , Chen, Kaizhi . SStyleGAN: a StyleGAN model for generating symmetrical lace images . (2024) . |
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To cope with the threat of image content tampering in real scenes, this paper develops a multi-view spatial-channel attention network (MSCA-Net), which can use multi-view features and multi-scale features to detect whether an image has been tampered with and predict tampered regions. By introducing the frequency domain view of the image, the model can use the noise distribution around the tampered region to learn semantically independent features and detect subtle tampering traces that are difficult to detect in the RGB domain. Secondly, a new Efficient Spatial-Channel Attention Module (ESCM) is proposed to capture the correlation between different channels and between global pixels. MSCA-Net improves the localization performance of tampered regions on real-scene images by generating segmentation masks step by step at multiple scales through a progressive guidance mechanism. MSCA-Net runs very fast and is capable of processing 1080P resolution images at 40FPS+. Extensive experimental results demonstrate the promising performance of MSCA-Net on both image-level and pixel-level tampering detection tasks. © 2024 SPIE. All rights reserved.
Keyword ：
Behavioral research Behavioral research Feature extraction Feature extraction Frequency domain analysis Frequency domain analysis Image enhancement Image enhancement Image segmentation Image segmentation Pixels Pixels
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| GB/T 7714 | Liu, Hanquan , Zhong, Shangping , Chen, Kaizhi . Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention [C] . 2024 . |
| MLA | Liu, Hanquan 等. "Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention" . (2024) . |
| APA | Liu, Hanquan , Zhong, Shangping , Chen, Kaizhi . Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention . (2024) . |
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Water quality prediction is of great significance in pollution control, prevention, and management. Deep learning models have been applied to water quality prediction in many recent studies. However, most existing deep learning models for water quality prediction are used for single-site data, only considering the time dependency of water quality data and ignoring the spatial correlation among multi-sites. This research defines and analyzes the non-aligned spatial correlations that exist in multi-site water quality data. Then deploy spatial-temporal graph convolution to process water quality data, which takes into account both the temporal and spatial correlation of multi-site water quality data. A multi-site water pollution prediction method called W-WaveNet is proposed that integrates adaptive graph convolution and Convolutional Neural Network, Long Short-Term Memory (CNN-LSTM). It integrates temporal and spatial models by interleaved stacking. Theoretical analysis shows that the method can deal with non-aligned spatial correlations in different time spans, which is suitable for water quality data processing. The model validates water quality data generated on two real river sections that have multiple sites. The experimental results were compared with the results of Support Vector Regression, CNN-LSTM, and Spatial-Temporal Graph Convolutional Networks (STGCN). It shows that when W-WaveNet predicts water quality over two river sections, the average Mean Absolute Error is 0.264, which is 45.2% lower than the commonly used CNN-LSTM model and 23.8% lower than the STGCN. The comparison experiments also demonstrate that W-WaveNet has a more stable performance in predicting longer sequences.
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| GB/T 7714 | Yang, Shaojun , Zhong, Shangping , Chen, Kaizhi . W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM [J]. | PLOS ONE , 2024 , 19 (3) . |
| MLA | Yang, Shaojun 等. "W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM" . | PLOS ONE 19 . 3 (2024) . |
| APA | Yang, Shaojun , Zhong, Shangping , Chen, Kaizhi . W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM . | PLOS ONE , 2024 , 19 (3) . |
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Machine learning has become an influential and effective tool in numerous civil engineering applications, especially in the field of structural health monitoring (SHM). Recently, the emergence of self-supervised learning has led to the development of many industries, and its accuracy and stability are superior to previous methods. Self-supervised learning learns generalizable information representation from unlabeled mixed data by solving pretext tasks, and this feature is exactly in line with the mixed and unlabeled data in the SHM field. This is of great significance to the improvement of detection accuracy in SHM practical applications. Therefore, this paper proposes a new self-supervised method for structural damage detection. The key to this method is that we use two self-supervised pretext tasks to learn the latent feature representation of the data, and we introduce homoscedastic uncertainty for automatically assigning weights to the two pretext tasks. The relative confidence between tasks is captured, the impact of noise on tasks is reduced, the pretext tasks can better learn data feature representation, and the purpose of improving the accuracy of damage detection is achieved. © 2023 SPIE.
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| GB/T 7714 | He, W. , Chen, K. , Zhong, S. . Self-supervised structural damage detection method based on pretext tasks learning feature representations [未知]. |
| MLA | He, W. 等. "Self-supervised structural damage detection method based on pretext tasks learning feature representations" [未知]. |
| APA | He, W. , Chen, K. , Zhong, S. . Self-supervised structural damage detection method based on pretext tasks learning feature representations [未知]. |
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Graph convolutional networks (GCNs) have achieved impressive results in many medical scenarios involving graph node classification tasks. However, there are difficulties in transfer learning for graph representation learning and graph network models. Most GNNs work only in a single domain and cannot transfer the learned knowledge to other domains. Coronary Heart Disease (CHD) is a high-mortality disease, and there are non-public and significant differences in CHD datasets for current research, which makes it difficult to perform unified transfer learning. Therefore, in this paper, we propose a novel adversarial domain-adaptive multichannel graph convolutional network (DAMGCN) that can perform graph transfer learning on cross-domain tasks to achieve cross-domain medical knowledge transfer on different CHD datasets. First, we use a two-channel GCN model for feature aggregation using local consistency and global consistency. Then, a uniform node representation is generated for different graphs using an attention mechanism. Finally, we provide a domain adversarial module to decrease the discrepancies between the source and target domain classifiers and optimize the three loss functions in order to accomplish source and target domain knowledge transfer. The experimental findings demonstrate that our model performs best on three CHD datasets, and its performance is greatly enhanced by graph transfer learning.
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| GB/T 7714 | Lin, Huizhong , Chen, Kaizhi , Xue, Yutao et al. Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN) [J]. | SCIENTIFIC REPORTS , 2023 , 13 (1) . |
| MLA | Lin, Huizhong et al. "Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN)" . | SCIENTIFIC REPORTS 13 . 1 (2023) . |
| APA | Lin, Huizhong , Chen, Kaizhi , Xue, Yutao , Zhong, Shangping , Chen, Lianglong , Ye, Mingfang . Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN) . | SCIENTIFIC REPORTS , 2023 , 13 (1) . |
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Recognition and localization of the fittings in overhead transmission line (OTL) images are the basis of fittings status detection and fault diagnosis. Most of the current recognition and localization of the fittings are based on object detection methods, which cannot accurately locate the fittings, and existing instance segmentation methods have limited accuracy for instance segmentation of fittings in complex OTL scenarios. To solve these problems, in the interleaved execution part, we inherit the idea of Hybrid Task Cascade (HTC) and add a direct information path to the same-stage box and mask branches to reinforce the coupled relationship between detection and segmentation; in the mask branch part, we sequentially apply the efficient channel attention (ECA) module and the dilated spatial attention (DSA) module and then insert them into the mask branch to improve the cross-stage information communication in the cascade architecture and mask prediction. Combining them results in Mixed Attention Interleaved Execution Cascade (MAIEC), a new cascade architecture for instance segmentation. Extensive experiments on the OTL fittings dataset reveal the effectiveness of the proposed method. The proposed MAIEC improves the AP of the box and mask predictions by respectively 2.0% and 1.5% compared to the strong HTC baseline. © 2023 IEEE.
Keyword ：
Image segmentation Image segmentation Object detection Object detection Overhead lines Overhead lines
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| GB/T 7714 | Yang, Yilong , Zhong, Shangping , Chen, Kaizhi . Mixed Attention Interleaved Execution Cascade Architecture for Fittings Instance Segmentation in Overhead Transmission Line Images [C] . 2023 : 228-235 . |
| MLA | Yang, Yilong et al. "Mixed Attention Interleaved Execution Cascade Architecture for Fittings Instance Segmentation in Overhead Transmission Line Images" . (2023) : 228-235 . |
| APA | Yang, Yilong , Zhong, Shangping , Chen, Kaizhi . Mixed Attention Interleaved Execution Cascade Architecture for Fittings Instance Segmentation in Overhead Transmission Line Images . (2023) : 228-235 . |
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In order to prevent the impact of difficult to recognize genuine and fake face changing images on people's lives and social stability, it is urgent to study efficient deepfake detection algorithms for such images. However, most existing deepfake detection algorithms require extracting a large number of video frames from dataset videos for training. To address this issue, this paper proposes a new deepfake detection algorithm based on a multi-Attentional model. Firstly, this article adds and fuses channel information to multiple attention maps generated by multiple spatial attention heads, enabling the model to comprehensively utilize information from various different features and provide richer feature representations. Secondly, in the texture enhancement section, DenseBlock is used to expand the receptive field and enhance the ability to extract shallow features, enabling the model to capture more detailed information. This article uses single frame real faces extracted from the public dataset FaceForensics++, a few sample dataset created from forged faces tampered with by DeepFake, and a dataset created from forged faces tampered with by our own image editing software to verify the performance of the proposed method. The experimental results show that on both datasets, AUC and ACC have increased by 1.8%, 2.1%, and 1.7%, 3.9%, respectively, compared to the original model. © 2023 ACM.
Keyword ：
channel information channel information deepfake detection deepfake detection Dense-Block Dense-Block Multi-Attentional model Multi-Attentional model
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| GB/T 7714 | Yang, T. , Chen, K. , Zhong, S. . Deepfake Detection Using Fusion Channel Information in a Multi-Attentional Model [未知]. |
| MLA | Yang, T. et al. "Deepfake Detection Using Fusion Channel Information in a Multi-Attentional Model" [未知]. |
| APA | Yang, T. , Chen, K. , Zhong, S. . Deepfake Detection Using Fusion Channel Information in a Multi-Attentional Model [未知]. |
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Coronary heart disease (CHD) has become one of the most serious public health issues due to its high morbidity and mortality rates. Most of the existing coronary heart disease risk prediction models manually extract features based on shallow machine learning methods. It only focuses on the differences between local patient features and ignores the interaction modeling between global patients. Its accuracy is still insufficient for individualized patient management strategies. In this paper, we propose CHD prediction as a graph node classification task for the first time, where nodes can represent individuals in potentially diseased populations and graphs intuitively represent associations between populations. We used an adaptive multi-channel graph convolutional neural network (AM-GCN) model to extract graph embeddings from topology, node features, and their combinations through graph convolution. Then, the adaptive importance weights of the extracted embeddings are learned by using an attention mechanism. For different situations, we model the relationship of the CHD population with the population graph and the K-nearest neighbor graph method. Our experimental evaluation explored the impact of the independent components of the model on the CHD disease prediction performance and compared it to different baselines. The experimental results show that our new model exhibits the best experimental results on the CHD dataset, with a 1.3% improvement in accuracy, a 5.1% improvement in AUC, and a 4.6% improvement in F1-score compared to the nongraph model.
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| GB/T 7714 | Xue, Yutao , Chen, Kaizhi , Lin, Huizhong et al. All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks [J]. | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE , 2022 , 2022 . |
| MLA | Xue, Yutao et al. "All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks" . | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022 (2022) . |
| APA | Xue, Yutao , Chen, Kaizhi , Lin, Huizhong , Zhong, Shangping . All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks . | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE , 2022 , 2022 . |
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Manifold learning is an important class of methods for nonlinear dimensionality reduction. Among them, the LLE optimisation goal is to maintain the relationship between local neighbourhoods in the original embedding manifold to reduce dimensionality, and NPE is a linear approximation to LLE. However, these two algorithms only consider maintaining the neighbour relationship of samples in low-dimensional space and ignore the global features between non-neighbour samples, such as the face shooting angle. Therefore, in order to simultaneously consider the nearest neighbour structure and global features of samples in nonlinear dimensionality reduction, it can be linearly calculated. This work provides a novel linear dimensionality reduction approach named non-neighbour and neighbour preserving embedding (NNNPE). First, we rewrite the objective function of the algorithm LLE based on the principle of our novel algorithm. Second, we introduce the linear mapping to the objective function. Finally, the mapping matrix is calculated by the method of the fast learning Mahalanobis metric. The experimental results show that the method proposed in this paper is effective.
Keyword ：
dimensionality reduction dimensionality reduction Machine learning Machine learning manifold learning manifold learning neighborhood preserving embedding neighborhood preserving embedding
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| GB/T 7714 | Chen, Kaizhi , Le, Chengpei , Zhong, Shangping et al. NNNPE: non-neighbourhood and neighbourhood preserving embedding [J]. | CONNECTION SCIENCE , 2022 , 34 (1) : 2615-2629 . |
| MLA | Chen, Kaizhi et al. "NNNPE: non-neighbourhood and neighbourhood preserving embedding" . | CONNECTION SCIENCE 34 . 1 (2022) : 2615-2629 . |
| APA | Chen, Kaizhi , Le, Chengpei , Zhong, Shangping , Guo, Longkun , Xu, Ge . NNNPE: non-neighbourhood and neighbourhood preserving embedding . | CONNECTION SCIENCE , 2022 , 34 (1) , 2615-2629 . |
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