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Abstract ：
In industrial manufacturing, defect detection is essential. Since the 2020's ViT (vision transformer) hit the scene, ViT has been increasingly used for defect detection tasks in the vision domain. The advantage of ViT over convolutional neural networks (CNNs) is its ability to capture global remote dependencies to learn better features. In addition to this, contrast learning based on self-supervised methods has been well used in defect detection tasks. In this study, we suggest a strategy for detecting fabric defects that combines transformer and contrast learning. First, we propose a new backbone network CViT (convolutional vision transformer), which is improved relative to ViT by adding a convolutional attention module to the ordinary transformer block structure while using depthwise separable convolution instead of linear projection to obtain q, k, and v for attention computation. Second, to compensate for the potential instability of CViT, instead of the 16 × 16 big convolutions used in the ViT, we use several stacked 3 × 3 tiny convolutions to divide each enhanced sample into a series of patches. Third, we incorporate conditional position encoding(CPE) and explore the impact of different position encodings on model performance. Finally, the effectiveness of our model is demonstrated on three classical public datasets for fabric fault detection. © 2024 SPIE. All rights reserved.
Keyword ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Defects Defects Encoding (symbols) Encoding (symbols) Fault detection Fault detection Learning systems Learning systems Signal encoding Signal encoding
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| GB/T 7714 | Xie, Huosheng , Zhao, Yuan . Self-supervised fabric defect detection model combined with transformer [C] . 2024 . |
| MLA | Xie, Huosheng 等. "Self-supervised fabric defect detection model combined with transformer" . (2024) . |
| APA | Xie, Huosheng , Zhao, Yuan . Self-supervised fabric defect detection model combined with transformer . (2024) . |
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Abstract ：
Defect detection has a wide range of applications in industry, and previous work has tended to be supervised learning, which typically requires a large number of samples. In this paper, we propose an unsupervised learning method that learns knowledge about normal images by distilling knowledge from a pre-trained expert network on ImageNet to a learner network of the same size. For a given input image, we use the differences in the features of the different layers of the expert network and learner network to detect and localize defects. We show that using comprehensive knowledge makes the differences between the two networks more apparent and that combining the differences in multi-level features can make the networks more generalizable. It's worth noting that we don't need to split the picture into patches to train, and we don't need to design the learner network additionally. Our general framework is relatively simple, yet has a good detection effect. We provide very competitive results on the MVTecAD dataset and DAGM dataset.
Keyword ：
Defect detection Defect detection Knowledge distillation Knowledge distillation Multi-level fusion Multi-level fusion Unsupervised learning Unsupervised learning
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| GB/T 7714 | Xie, Huosheng , Xiao, Yan . Multiresolution Knowledge Distillation and Multi-level Fusion for Defect Detection [J]. | GREEN, PERVASIVE, AND CLOUD COMPUTING, GPC 2022 , 2023 , 13744 : 178-191 . |
| MLA | Xie, Huosheng 等. "Multiresolution Knowledge Distillation and Multi-level Fusion for Defect Detection" . | GREEN, PERVASIVE, AND CLOUD COMPUTING, GPC 2022 13744 (2023) : 178-191 . |
| APA | Xie, Huosheng , Xiao, Yan . Multiresolution Knowledge Distillation and Multi-level Fusion for Defect Detection . | GREEN, PERVASIVE, AND CLOUD COMPUTING, GPC 2022 , 2023 , 13744 , 178-191 . |
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Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM. © 2023 IEEE.
Keyword ：
Computer vision Computer vision Image enhancement Image enhancement Semantics Semantics
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| GB/T 7714 | Chen, Jinjian , Li, Zuoyong , Lai, Taotao et al. Semantic Correspondence with Peripheral Position Coding [C] . 2023 : 329-334 . |
| MLA | Chen, Jinjian et al. "Semantic Correspondence with Peripheral Position Coding" . (2023) : 329-334 . |
| APA | Chen, Jinjian , Li, Zuoyong , Lai, Taotao , Xie, Huosheng . Semantic Correspondence with Peripheral Position Coding . (2023) : 329-334 . |
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Accurate short-term forecasting of intensive rainfall has high practical value but remains difficult to achieve. Based on deep learning and spatial-temporal sequence predictions, this paper proposes a hierarchical dynamic graph network. To fully model the correlations among data, the model uses a dynamically constructed graph convolution operator to model the spatial correlation, a recurrent structure to model the time correlation, and a hierarchical architecture built with graph pooling to extract and fuse multi-level feature spaces. Experiments on two datasets, based on the measured cumulative rainfall data at a ground station in Fujian Province, China, and the corresponding numerical weather grid product, show that this method can model various correlations among data more effectively than the baseline methods, achieving further improvements owing to reversed sequence enhancement and low-rainfall sequence removal.
Keyword ：
graph convolutional network graph convolutional network hierarchical dynamic graph network hierarchical dynamic graph network numerical weather prediction numerical weather prediction short-term intensive rainfall forecast short-term intensive rainfall forecast spatial-temporal sequence prediction spatial-temporal sequence prediction
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| GB/T 7714 | Xie, Huosheng , Zheng, Rongyao , Lin, Qing . Short-Term Intensive Rainfall Forecasting Model Based on a Hierarchical Dynamic Graph Network [J]. | ATMOSPHERE , 2022 , 13 (5) . |
| MLA | Xie, Huosheng et al. "Short-Term Intensive Rainfall Forecasting Model Based on a Hierarchical Dynamic Graph Network" . | ATMOSPHERE 13 . 5 (2022) . |
| APA | Xie, Huosheng , Zheng, Rongyao , Lin, Qing . Short-Term Intensive Rainfall Forecasting Model Based on a Hierarchical Dynamic Graph Network . | ATMOSPHERE , 2022 , 13 (5) . |
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Abstract ：
In practice, accurate and timely forecasting of short-term intense rainfall is critical, but the problem is extremely difficult because to its complicated spatial-temporal association. Although several spatial-temporal series forecasting methods have been used to rainfall prediction, these models continue to suffer from inadequate modeling of data's complicated intrinsic connection. We provide a new short-term intense rainfall prediction model that use two graph generators to model data correlations under distinct semantics, followed by a graph convolution module for information integration to fully extract data spatial-temporal information. Finally, a variant of recurrent neural network is employed to extract the temporal dependence. The experimental results on both datasets show that the model can model the spatial and temporal dependence across the data more effectively than the baseline model, and further improve the model's predictive performance for short-term intense rainfall. © 2022 ACM.
Keyword ：
Convolution Convolution Data mining Data mining Graph neural networks Graph neural networks Rain Rain Recurrent neural networks Recurrent neural networks Semantics Semantics Semantic Web Semantic Web Weather forecasting Weather forecasting
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| GB/T 7714 | Xie, Huo Shen , Wang, Weijie . Long Short-term Dynamic Graph Neural Networks: For short-term intense rainfall forecasting [C] . 2022 : 74-80 . |
| MLA | Xie, Huo Shen et al. "Long Short-term Dynamic Graph Neural Networks: For short-term intense rainfall forecasting" . (2022) : 74-80 . |
| APA | Xie, Huo Shen , Wang, Weijie . Long Short-term Dynamic Graph Neural Networks: For short-term intense rainfall forecasting . (2022) : 74-80 . |
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Abstract ：
As a pivotal technique of fashion visual analysis, fashion landmark detection has attracted extensive attention in recent years. However, prior works often ignore the importance of structural layout information for fashion landmark detection, which leads to ambiguous detection results of hard landmarks. In this paper, we propose a Layout-Aware Bidirectional Transfer Network(LBTNet) which first combines the layout features learning with a powerful human pose estimation backbone - HRNet. The LBTNet can learn the layout information by our proposed Group-wise Layout Embedded Module(GLEM) which can model the dependency among fashion landmarks on the convolutional layer and perform information passing among the adjacent landmarks. We also design a novel head structure called Bidirectional Transfer Module(BTM) to capture global semantic information of fashion landmarks through a bidirectional transmission path. Therefore, the LBTNet can accurately detect these hard landmarks (e.g. occluded landmarks and invisible landmarks). And the experimental results on two large- scale fashion datasets show that our LBTNet outperforms the state-of-the-art methods by a large margin.
Keyword ：
bidirectional transfer module bidirectional transfer module Fashion landmark detection Fashion landmark detection group-wise layout embedded module group-wise layout embedded module HRNet HRNet
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| GB/T 7714 | Xie, Huosheng , Chen, Jiaqi . Layout-Aware Bidirectional Transfer Network for Fashion Landmark Detection [J]. | THIRTEENTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2021) , 2022 , 12083 . |
| MLA | Xie, Huosheng et al. "Layout-Aware Bidirectional Transfer Network for Fashion Landmark Detection" . | THIRTEENTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2021) 12083 (2022) . |
| APA | Xie, Huosheng , Chen, Jiaqi . Layout-Aware Bidirectional Transfer Network for Fashion Landmark Detection . | THIRTEENTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2021) , 2022 , 12083 . |
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Abstract ：
The fabric defect detection algorithm based on object detection has become a research hotspot. The method based on the Single Shot MultiBox Detector (SSD) model has a fast detection speed, but the detection accuracy is insufficient. To balance the detection speed and accuracy of the model and meet the actual needs of the industry, an improved fabric defect detection algorithm based on SSD is proposed in this study. The Fully Convolutional Squeeze-and-Excitation (FCSE) block is added into the traditional SSD to improve the detection accuracy of the model. The number of default boxes was adjusted to accommodate the detection of long strip defects on fabric surface. Experimental results on the TILDA and Xuelang dataset confirm that our detection method based on SSD efficiently detected various fabric defects.
Keyword ：
Computer Vision Computer Vision Fabric Defects Fabric Defects FCSE FCSE Object Detection Object Detection SSD SSD
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| GB/T 7714 | Xie, Huosheng , Zhang, Yafeng , Wu, Zesen . An Improved Fabric Defect Detection Method Based on SSD [J]. | AATCC JOURNAL OF RESEARCH , 2021 , 8 (1_SUPPL) : 182-191 . |
| MLA | Xie, Huosheng et al. "An Improved Fabric Defect Detection Method Based on SSD" . | AATCC JOURNAL OF RESEARCH 8 . 1_SUPPL (2021) : 182-191 . |
| APA | Xie, Huosheng , Zhang, Yafeng , Wu, Zesen . An Improved Fabric Defect Detection Method Based on SSD . | AATCC JOURNAL OF RESEARCH , 2021 , 8 (1_SUPPL) , 182-191 . |
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Abstract ：
Dimensional sentiment analysis has received considerable attention because it can represent affective states as continuous numerical values on multiple dimensions such as valence (positive-negative) and arousal (excited-calm). Compared to the categorical approach, which represents affective states as several discrete classes (e.g., positive and negative), the dimensional approach can provide more fine-grained (real-valued) sentiment analysis. Traditional approaches to predicting dimensional sentiment scores typically treat each dimension independently without consideration of relations between dimensions. In fact, different dimensions may correlate with each other. For example, expressions with a higher valence score usually have a higher arousal score, And higher irony expressions usually have a lower valence score. Such relations between dimensions are useful for dimension score prediction. To this end, this study proposes a multi-dimensional relation model to incorporate relations between dimensions into deep neural networks for dimension score prediction. The proposed method has two modes: internal and external. The internal mode incorporates the relations between dimensions into sentence representations before prediction, whereas the external mode builds a linear regression model that can capture the relations between dimensions to refine the predicted scores after prediction. To evaluate the proposed method, we created a Chinese three-dimensional corpus with valence-arousal-irony (VAI) ratings. Experiments using various neural network architectures demonstrate that the proposed multi-dimensional relation model outperformed those that treat each dimension independently. In addition, the internal mode outperformed the external mode, and a combination of the two modes achieved the best performance. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword ：
Multi-dimensional relations Multi-dimensional relations Neural networks Neural networks Sentiment analysis Sentiment analysis Valence-arousal-irony ratings Valence-arousal-irony ratings
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| GB/T 7714 | Xie, Housheng , Lin, Wei , Lin, Shuying et al. A multi-dimensional relation model for dimensional sentiment analysis [J]. | INFORMATION SCIENCES , 2021 , 579 : 832-844 . |
| MLA | Xie, Housheng et al. "A multi-dimensional relation model for dimensional sentiment analysis" . | INFORMATION SCIENCES 579 (2021) : 832-844 . |
| APA | Xie, Housheng , Lin, Wei , Lin, Shuying , Wang, Jin , Yu, Liang-Chih . A multi-dimensional relation model for dimensional sentiment analysis . | INFORMATION SCIENCES , 2021 , 579 , 832-844 . |
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Short-term intensive rainfall (3-h rainfall amount > 30 mm) is a destructive weather phenomenon that is poorly predicted using traditional forecasting methods. In this study, we propose a model using European Center for Medium-Range Weather Forecasts (ECMWF) data and a machine learning framework to improve the ability of short-term intensive rainfall forecasting in Fujian Province, China. ECMWF forecast data and ground observation station data (2015-2018) were interpolated using a radial basis function, outliers were processed, and the data were blocked according to the monthly cumulative rainfall and forecast window. Subsequently, the box difference index was used to select features for each data block. As short-term intensive rainfall events are rare, a data processing method based on the K-means and generative adversarial nets was used to address data imbalances in the rainfall distribution. Finally, focal loss object detection was combined with a deep belief network to construct the short-term intensive rainfall classification model. The results show that the data preprocessing method and resampling method used in this study were effective. Furthermore, the classification model was superior to other machine learning methods for predicting short-term intensive rainfall.
Keyword ：
Deep belief network Deep belief network ECMWF ECMWF Fujian Province Fujian Province Generative adversarial nets Generative adversarial nets Machine learning Machine learning Short-term intensive rainfall forecast Short-term intensive rainfall forecast
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| GB/T 7714 | Xie, Huosheng , Wu, Lidong , Xie, Wei et al. Improving ECMWF short-term intensive rainfall forecasts using generative adversarial nets and deep belief networks [J]. | ATMOSPHERIC RESEARCH , 2021 , 249 . |
| MLA | Xie, Huosheng et al. "Improving ECMWF short-term intensive rainfall forecasts using generative adversarial nets and deep belief networks" . | ATMOSPHERIC RESEARCH 249 (2021) . |
| APA | Xie, Huosheng , Wu, Lidong , Xie, Wei , Lin, Qing , Liu, Ming , Lin, Yongjing . Improving ECMWF short-term intensive rainfall forecasts using generative adversarial nets and deep belief networks . | ATMOSPHERIC RESEARCH , 2021 , 249 . |
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In recent research, the defect detection algorithm based on the fully-supervised object detection model has become one of the research hotspots and has achieved good results. However, fully-supervised object detection models require image-level and localization-level labels. Obtaining these labels requires a great deal of manpower. Therefore, this paper proposes a dual path defect detection network (DPNET) based on weakly supervised object detection model, which aims to identify the classification label and carry on localization for defects merely by using image-level labels. Firstly, the paper employs the deep convolutional residual network ResNet-50 as a feature classification network for defect classification. Secondly, we designed a localization network based on the global average-max pooling class activation map (GAM-CAM) and the Full Convolutional Channel Attention (FCCA) for defect localization, which can improve the defect localization accuracy. Experimental results on the DAGM dataset confirm that the proposed detection model is able to efficiently detect defects. © 2021, Springer Nature Switzerland AG.
Keyword ：
Cams Cams Chemical activation Chemical activation Classification (of information) Classification (of information) Computer vision Computer vision Convolution Convolution Defects Defects Object detection Object detection Object recognition Object recognition
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| GB/T 7714 | Xie, Huosheng , Lin, ShuFeng . A Weakly Supervised Defect Detection Based on Dual Path Networks and GMA-CAM [C] . 2021 : 467-478 . |
| MLA | Xie, Huosheng et al. "A Weakly Supervised Defect Detection Based on Dual Path Networks and GMA-CAM" . (2021) : 467-478 . |
| APA | Xie, Huosheng , Lin, ShuFeng . A Weakly Supervised Defect Detection Based on Dual Path Networks and GMA-CAM . (2021) : 467-478 . |
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