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学者姓名：王长缨
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Abstract ：
Identifying delicate yet discriminative features is the main task of fine-grained visual categorization (FGVC). The Transformer's long-range receptive field makes it appropriate for FGVC. Existing Transformer-based efforts heavily rely on attention weights to mine fine-grained local features. However, the approach will highlight irrelevant, even adversarial, regions. In this paper, we propose a new formulation that optimizes the token-importance indicator to distinguish the contribution and suppression of tokens and effectively select discriminative patches. Given a network, the Part Selection Module(PSM) introduces gradients to attention weights for token polarity and utilizes the fusion rule to aggregate multi-layers information. The Dual-Polarity ViT(DP ViT) can reduce misleading attention and focus on subtle yet discriminative parts. Our method outperforms existing methods and improves baselines. © 2023 IEEE.
Keyword ：
Computer vision Computer vision
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| GB/T 7714 | Guo, Tingjiang , Cheng, Li , Wang, Changying et al. Dual-Polarity ViT for Fine-Grained Visual Categorization [J]. | Proceedings - 2023 8th International Conference on Multimedia Communication Technologies, ICMCT 2023 , 2023 : 50-56 . |
| MLA | Guo, Tingjiang et al. "Dual-Polarity ViT for Fine-Grained Visual Categorization" . | Proceedings - 2023 8th International Conference on Multimedia Communication Technologies, ICMCT 2023 (2023) : 50-56 . |
| APA | Guo, Tingjiang , Cheng, Li , Wang, Changying , Chen, Riqing , Zhang, Huaiyong , Lan, Linyi . Dual-Polarity ViT for Fine-Grained Visual Categorization . | Proceedings - 2023 8th International Conference on Multimedia Communication Technologies, ICMCT 2023 , 2023 , 50-56 . |
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CT检查在新冠肺炎诊断中起着重要作用，为了能够在有限的CT胸部图像集中获得更多有关新冠肺炎的特征信息、建立更加敏感通用的诊断模型，提出了融合CT图像频域特征的双路网络模型（Dp-Net），该模型主干部分采用ResNet网络模型，并将卷积神经网络的训练过程分为两个部分，一部分提取CT图像空间域的特征，另一部分通过傅里叶变换提取频率域上的特征，将两者训练的结果按照一定的权重进行融合，融合后再由Layer4模块进行一次特征提取。在公开的COVID-CT数据集上与ResNet、VGG等传统的CNN模型进行了比较，也与Self-Trans和LA-DNN等一些改进的CNN模型进行了比较，并对不同权重的融合方案进行了比较，实验结果表明提出的Dp-Net模型在各种评价指标上取得了更好的结果。
Keyword ：
CT图像 CT图像 卷积神经网络 卷积神经网络 新冠肺炎 新冠肺炎 特征融合 特征融合 频率域 频率域
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| GB/T 7714 | 杨宇航 , 林敏 , 王长缨 et al. 融合频率域特征的双路网络模型诊断新冠肺炎 [J]. | 计算机工程与应用 , 2023 , 59 (05) : 321-327 . |
| MLA | 杨宇航 et al. "融合频率域特征的双路网络模型诊断新冠肺炎" . | 计算机工程与应用 59 . 05 (2023) : 321-327 . |
| APA | 杨宇航 , 林敏 , 王长缨 , 钟一文 . 融合频率域特征的双路网络模型诊断新冠肺炎 . | 计算机工程与应用 , 2023 , 59 (05) , 321-327 . |
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Abstract ：
CT检查在新冠肺炎诊断中起着重要作用,为了能够在有限的CT胸部图像集中获得更多有关新冠肺炎的特征信息、建立更加敏感通用的诊断模型,提出了融合CT图像频域特征的双路网络模型(Dp-Net),该模型主干部分采用ResNet网络模型,并将卷积神经网络的训练过程分为两个部分,一部分提取CT图像空间域的特征,另一部分通过傅里叶变换提取频率域上的特征,将两者训练的结果按照一定的权重进行融合,融合后再由Layer4模块进行一次特征提取。在公开的COVID-CT数据集上与ResNet、VGG等传统的CNN模型进行了比较,也与Self-Trans和LA-DNN等一些改进的CNN模型进行了比较,并对不同权重的融合方案进行了比较,实验结果表明提出的Dp-Net模型在各种评价指标上取得了更好的结果。
Keyword ：
CT图像 CT图像 卷积神经网络 卷积神经网络 新冠肺炎 新冠肺炎 特征融合 特征融合 频率域 频率域
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| GB/T 7714 | Yang, Yuhang , Lin, Min , Wang, Changying et al. 融合频率域特征的双路网络模型诊断新冠肺炎 [J]. | 计算机工程与应用 , 2023 , 59 (5) : 321-327 . |
| MLA | Yang, Yuhang et al. "融合频率域特征的双路网络模型诊断新冠肺炎" . | 计算机工程与应用 59 . 5 (2023) : 321-327 . |
| APA | Yang, Yuhang , Lin, Min , Wang, Changying , Zhong, Yiwen . 融合频率域特征的双路网络模型诊断新冠肺炎 . | 计算机工程与应用 , 2023 , 59 (5) , 321-327 . |
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本发明涉及信息处理技术领域，具体是一种边缘计算的水稻病虫害图像识别系统，包括高算力GPU开发板、4G模块、摄像头、舵机、舵机驱动板、二自由度云台，并使用太阳能板进行供电。水稻病虫害图像识别采用深度学习方法，通过知识蒸馏方法对大模型进行压缩并提高其泛化能力，并将训练得到的模型部署在现场高算力嵌入式开发板。通过现场端和云端的应用层传输协议，将现场摄像头采集的原图像和识别后的结果，连同摄像头编号、当前时间等信息一起传输至云端。同时，设计前端网页界面，能够通过曲线图的形式对识别结果进行展示，还可以进行界面的切换，通过前端远程控制现场摄像头转动的角度，实现了水稻病虫害的自动识别，避免了人工勘察的繁琐。
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| GB/T 7714 | 郑宇琛 , 陈君延 , 罗正龙 et al. 一种边缘计算的水稻病虫害图像识别系统 : CN202211446099.9[P]. | 2022-11-18 . |
| MLA | 郑宇琛 et al. "一种边缘计算的水稻病虫害图像识别系统" : CN202211446099.9. | 2022-11-18 . |
| APA | 郑宇琛 , 陈君延 , 罗正龙 , 钟日深 , 林知宇 , 程丽 et al. 一种边缘计算的水稻病虫害图像识别系统 : CN202211446099.9. | 2022-11-18 . |
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Abstract ：
The aim of image fusion is to synthesize the information from multiple images to provide richer information for understanding human image content. Since the visual saliency of the images is the basis for understanding the content of the images, understanding and emphasizing image fusion information of different modes is the key of multi-modal image fusion. In the field of infrared and visible image fusion, the fusion logic of current research relies principally on understanding the results of fusion, subjectively emphasizing the strong contrast region in the infrared image and the texture detail in the visible image. The emphais on content, based on people's subjective understanding, reflects people's demands and understanding of the fusion results, but this subjective emphasis is not sufficiently complete to pay attention to the content of the source images, and ignores certain details, which affects the fusion effect. Therefore, this article proposes to learn the human understanding of the saliency features of the image through deep neural networks to obtain more complete fusion features than subjective emphasis. These features implicitly reflect human vision's perception of the importance of different modal features in fusion. ASNet has a great performance in salient target inference from the human gaze, we therefore introduce it to obtain salient features more coherent with human visual understanding in source images, which are used as attention prior information. © 2022 IEEE.
Keyword ：
Deep neural networks Deep neural networks Image fusion Image fusion Infrared imaging Infrared imaging Textures Textures
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| GB/T 7714 | Qingwei, Zhang , Fangfang, Fan , Yiwen, Zhong et al. An Implicit Salienct Guided Infrared And Visible Image Fusion Method [J]. | International Conference on Communication Technology Proceedings, ICCT , 2022 , 2022-November-November : 1612-1616 . |
| MLA | Qingwei, Zhang et al. "An Implicit Salienct Guided Infrared And Visible Image Fusion Method" . | International Conference on Communication Technology Proceedings, ICCT 2022-November-November (2022) : 1612-1616 . |
| APA | Qingwei, Zhang , Fangfang, Fan , Yiwen, Zhong , Changying, Wang , Zhongjie, Xiao . An Implicit Salienct Guided Infrared And Visible Image Fusion Method . | International Conference on Communication Technology Proceedings, ICCT , 2022 , 2022-November-November , 1612-1616 . |
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Due to the significant intra-class variability and high inter-class similarity of corals in underwater environments, it is extremely difficult to accurately classify coral image. Hence, a coral image classification approach based on dual-branch feature fusion (DBFF) neural network is proposed, one branch is the residual network to fuse high-dimensional and low-dimensional features, and the other one is multi-scale feature extraction using pyramid convolution. Then, the features of the two branches are fused to obtain richer global features with detailed information to better distinguish similar species. The experimentation result on the coral dataset StructureRSMAS shows that classification accuracy of DBFF is 92.40%, which achieves a higher classification accuracy than that of the existing methods. © 2022 IEEE.
Keyword ：
Classification (of information) Classification (of information) Convolution Convolution Image classification Image classification Image fusion Image fusion
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| GB/T 7714 | Bai, Dandan , Lin, Jiaxiang , Huang, Yigong et al. A Novel Approach for Coral Image Classification with Dual-branch Feature Fusion Neural Network [J]. | Proceedings - 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2022 , 2022 : 155-160 . |
| MLA | Bai, Dandan et al. "A Novel Approach for Coral Image Classification with Dual-branch Feature Fusion Neural Network" . | Proceedings - 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2022 (2022) : 155-160 . |
| APA | Bai, Dandan , Lin, Jiaxiang , Huang, Yigong , Wang, Changying , Wu, Jianwei . A Novel Approach for Coral Image Classification with Dual-branch Feature Fusion Neural Network . | Proceedings - 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2022 , 2022 , 155-160 . |
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Face detection has been well studied for many years and one of the remaining challenges is to detect complex occluded faces in a real-world environment. Hence, this paper introduces a Multi-task Perceptual Occlusion Face Detection framework with a semantic attention network (MTOFD), which can detect face under complex occlusion conditions, especially, it takes the discrimination of occlusion type as a learning task, and use occlusion semantic information to improve face detection. In addition, an adaptive semantic attention network is employed to solve the conflict problem caused by multi-task in feature fusion, in which the potential semantic information of the occlusion task is learned adaptively, and the most important semantic information is selected and aggregated automatically to the task of occlusion face detection. Finally, MTOFD is tested and compared with some typical algorithms, such as FAN and AOFD, and it is found that our algorithm achieves state-of-the-art performance on dataset MAFA.
Keyword ：
Multi-task learning Multi-task learning Occlusion face detection Occlusion face detection Semantic attention Semantic attention
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| GB/T 7714 | Shen, Lian , Lin, Jia-Xiang , Wang, Chang-Ying . Multi-task Perceptual Occlusion Face Detection with Semantic Attention Network [J]. | NEURAL INFORMATION PROCESSING, ICONIP 2021, PT I , 2021 , 13108 : 129-140 . |
| MLA | Shen, Lian et al. "Multi-task Perceptual Occlusion Face Detection with Semantic Attention Network" . | NEURAL INFORMATION PROCESSING, ICONIP 2021, PT I 13108 (2021) : 129-140 . |
| APA | Shen, Lian , Lin, Jia-Xiang , Wang, Chang-Ying . Multi-task Perceptual Occlusion Face Detection with Semantic Attention Network . | NEURAL INFORMATION PROCESSING, ICONIP 2021, PT I , 2021 , 13108 , 129-140 . |
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In spite of great success in many visual recognition tasks achieved by recent deep models, they performed poorly at low resolution conditions. low-resolution image recognition is still a challenging problem. The massive loss of image detail information is the ultimate cause of this problem. However, the loss of individual instance details has little effect on the relationship between instances.Therefore, we propose a multi-level relational distillation method to solve the low resolution identification problem. Based on the teacher student framework of knowledge distillation, It transfer knowledge from teacher to student in two steps. The first step is to train the auxiliary network through the teacher network to remove the redundancy of the model structure. In the second step, the auxiliary network as a new teacher guided low-resolution student network. In order to better learn the behavior of the teacher network, we propose a multi-level instance relationship loss function. It is divided into three different levels: central angle relationship, inter-class angle relationship, and intra-class angle relationship. Respectively it transfer the relationship between instances on different scales, so as to minimize the loss of the image caused by the loss of detailed information. Finally, we carry out experiments on the low resolution image recognition data set CIFAR100 and CIFAR10. The results of experiments have proved that our method can effectively deal with low resolution recognition problems. © 2021 ACM.
Keyword ：
Distillation Distillation Education computing Education computing Image recognition Image recognition Students Students
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| GB/T 7714 | Shen, Lian , Lin, JiaXiang , Bai, DanDan et al. Multi-level Relational Knowledge Distillation for Low Resolution Image Recognition [J]. | ACM International Conference Proceeding Series , 2021 : 31-35 . |
| MLA | Shen, Lian et al. "Multi-level Relational Knowledge Distillation for Low Resolution Image Recognition" . | ACM International Conference Proceeding Series (2021) : 31-35 . |
| APA | Shen, Lian , Lin, JiaXiang , Bai, DanDan , Zhang, ZhenChang , Wang, ChangYing , Lei, Xiang . Multi-level Relational Knowledge Distillation for Low Resolution Image Recognition . | ACM International Conference Proceeding Series , 2021 , 31-35 . |
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Accent classification plays a vital role in the study of automatic speech recognition and the history of language development. Due to the complexity of Chinese structure and the diversity of Chinese accents, general accent classification methods cannot be effectively used for Chinese accent classification. This article proposes an end-to-end classification method. This method first learns a filter bank with variable window width from the original waveform. Secondly, the distinguishable features are extracted according to the different time-frequency aggregations in the voice signal. Finally, we use the temporal convolutional attention network to solve insufficient contextual information for Chinese accent classification to achieve accent feature classification. The effectiveness of this method is evaluated on the Common voice corpus. The results show that the method has better generalization performance. Compared with the baseline method, the English and Chinese speech data set accuracy increased by 6.27% and 26.11%, respectively. © 2021 ACM.
Keyword ：
Classification (of information) Classification (of information) Convolution Convolution Speech recognition Speech recognition
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| GB/T 7714 | Wu, Zixuan , Wang, Changying , Xue, Honghui et al. An End-to-End Chinese Accent Classification Method [J]. | ACM International Conference Proceeding Series , 2021 : 163-168 . |
| MLA | Wu, Zixuan et al. "An End-to-End Chinese Accent Classification Method" . | ACM International Conference Proceeding Series (2021) : 163-168 . |
| APA | Wu, Zixuan , Wang, Changying , Xue, Honghui , Shen, Lian , Wang, Zhihong , Chen, Junyan . An End-to-End Chinese Accent Classification Method . | ACM International Conference Proceeding Series , 2021 , 163-168 . |
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The presence of tortuosity in the retinal vessels is crucial in the diagnosis of ocular fundus disorders. There are numerous methods for computing the tortuosity of the retinal arteries available today, all of which have yielded impressive results. However, they usually divide vessels into smaller vascular structures to calculate local tortuosities, which are then weighted summed to get the global tortuosity of the entire vessel. The approach of local division on a two-dimensional image weakens local vessel tortuosity information and makes it unable to accurately portray the vessel's tortuosity. Hence, we propose a wavelet transform-based multi-scale approach for evaluating the tortuosity of fundus vessels in order to investigate the differences between normal and pathological vessels in terms of spatial tortuous properties. To acquire the skeleton of the fundus vessels, we apply the Zhang-Suen method to refine retinal vessel images segregated by experts. The vascular skeletons are then converted into one-dimension signals, on which we carry out a wavelet transform to yield vascular tortuosity of different scales, which is further evaluated with entropy. The results of the experiments reveal that the suggested tortuosity measure can effectively classify the curvature of blood vessel segments and blood vessel networks. © 2021 IEEE.
Keyword ：
Blood Blood Blood vessels Blood vessels Computerized tomography Computerized tomography Entropy Entropy Musculoskeletal system Musculoskeletal system Ophthalmology Ophthalmology Wavelet transforms Wavelet transforms
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| GB/T 7714 | Fan, FangFang , He, JiaXing , Wang, ChangYing et al. Multi-scale Retinal Vessel Tortuosity Measurement Based on Wavelet Transform [J]. | Proceedings - 11th International Conference on Information Technology in Medicine and Education, ITME 2021 , 2021 : 404-408 . |
| MLA | Fan, FangFang et al. "Multi-scale Retinal Vessel Tortuosity Measurement Based on Wavelet Transform" . | Proceedings - 11th International Conference on Information Technology in Medicine and Education, ITME 2021 (2021) : 404-408 . |
| APA | Fan, FangFang , He, JiaXing , Wang, ChangYing , Zhang, ZhenChang , Zhang, QingWei . Multi-scale Retinal Vessel Tortuosity Measurement Based on Wavelet Transform . | Proceedings - 11th International Conference on Information Technology in Medicine and Education, ITME 2021 , 2021 , 404-408 . |
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