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fine-grained classification of Remote Sensing Ship Images Based on Improved VAN

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Computers, Materials & Continua 2023年第11期77卷 1985-2007页

作者： Guoqing Zhou Liang Huang Qiao Sun College of Electronic Engineering Naval University of EngineeringWuhan430033China

The remote sensing ships’fine-grained classification technology makes it possible to identify certain ship types in remote sensing images,and it has broad application prospects in civil and military ***,the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated *** is still an opportunity for future enhancement of the classification *** solve the challenges brought by the above characteristics,this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network(VAN-MR)for fine-grained classification *** the complex background of remote sensing images,the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model’s feature extraction ability of interest targets and improve the classification performance of remote sensing ship *** the problem of multi-grained feature mixing in remote sensing images,the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship *** parallel network has different depths,considering both high-level and lowlevel semantic *** model achieves better classification performance in remote sensing ship images with multi-granularity ***,the model achieves 88.73%and 94.56%accuracy on the public fine-grained ship collection-23(FGSC-23)and FGSCR-42 datasets,respectively,while the parameter size is only 53.47 M,the floating point operations is 9.9 *** experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.

关键词： fine-grained classification metaformer remote sensing residual ship image

A new dataset of dog breed images and a benchmark for fine-grained classification

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Computational Visual Media 2020年第4期6卷 477-487页

作者： Ding-Nan Zou Song-Hai Zhang Tai-Jiang Mu Min Zhang Department of Computer Science and Technology BNRistTsinghua UniversityBeijing 100084China NaJiu Company Hunan 410022China Harvard Medical School Brigham and Women’s HospitalBostonMA 02115USA

In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. It is currently the largest dataset for fine-grained classification of dogs, including 130 dog breeds and 70,428 real-world images. It has only one dog in each image and provides annotated bounding boxes for the whole body and head. In comparison to previous similar datasets, it contains more breeds and more carefully chosen images for each breed. The diversity within each breed is greater,with between 200 and 7000+ images for each *** of the whole body and head makes the dataset not only suitable for the improvement of finegrained image classification models based on overall features, but also for those locating local informative parts. We show that dataset provides a tough challenge by benchmarking several state-of-the-art deep neural models. The dataset is available for academic purposes at https://***/ThuDogs/.

关键词： fine-grained classification dog dataset benchmark

A Novel fine-grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network

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Journal of Computer Science & Technology 2018年第2期33卷 335-350页

作者： Qian Wang You-Dong Ding School of Computer Enqineering and Science Shan qhai University Shanqhai 200072 China Information Center Criminal Investigation Department of Shanghai Public Security Bureau Shanghai 200083 China Shanghai Engineering Research Center of Motion Picture Special Effects Shanghai 200072 China Shanghai Film Academy Shanghai University Shanghai 200072 China

In this paper, we propose a locally enhanced PCANet neural network for fine-grained classification of vehicles. The proposed method adopts the PCANet unsupervised network with a smaller number of layers and simple parameters compared with the majority of state-of-the-art machine learning methods. It simplifies calculation steps and manual labeling, and enables vehicle types to be recognized without time-consuming training. Experimental results show that compared with the traditional pattern recognition methods and the multi-layer CNN methods, the proposed method achieves optimal balance in terms of varying scales of sample libraries, angle deviations, and training speed. It also indicates that introducing appropriate local features that have different scales from the general feature is very instrumental in improving recognition rate. The 7-angle in 180° （12-angle in 360°） classification modeling scheme is proven to be an effective approach, which can solve the problem of suffering decrease in recognition rate due to angle deviations, and add the recognition accuracy in practice.

关键词： fine-grained classification PCANet local enhancement vehicle type recognition

Understanding and Generating Ultrasound Image Description

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Journal of Computer Science & Technology 2018年第5期33卷 1086-1100页

作者： Xian-Huu Zeng Bang-Gui Liu Meng Zhou School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China

To understand the content of ultrasound images more conveniently and more quickly, in this paper, we propose a coarse-to-fine ultrasound image captioning ensemble model, which can automatically generate the annotation text that is composed of relevant n-grams to describe the disease information in the ultrasound images. First, the organs in the ultrasound images are detected by the coarse classification model. Second, the ultrasound images are encoded by the corresponding fine-grained classification model according to the organ labels. Finally, we input the encoding vectors to the language generation model, and the language generation model generates automatically annotation text to describe the disease information in the ultrasound images. In our experiments, the encoding model can obtain the high accuracy rate in the ultrasound image recognition. And the language generation model can automatically generate high-quality annotation text. In practical applications, the coarse-to-fine ultrasound image captioning ensemble model can help patients and doctors obtain the well understanding of the contents of ultrasound images.

关键词： ultrasound image fine-grained classification image captioning

Multi-Branch Deepfake Detection Algorithm Based on fine-grained Features

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Computers, Materials & Continua 2023年第10期77卷 467-490页

作者： Wenkai Qin Tianliang Lu Lu Zhang Shufan Peng Da Wan School of Information Network Security People’s Public Security University of ChinaBeijing100038China Department of Investigation Shandong Police CollegeJinan250200China

With the rapid development of deepfake technology,the authenticity of various types of fake synthetic content is increasing rapidly,which brings potential security threats to people’s daily life and social ***,most algorithms define deepfake detection as a binary classification problem,i.e.,global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or ***,the differences between real and fake samples are often subtle and local,and such global feature-based detection algorithms are not optimal in efficiency and *** this end,to enhance the extraction of forgery details in deep forgery samples,we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained ***,to address the critical problem in locating discriminative feature regions in fine-grained classification tasks,we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature ***,using information complementation,we introduce a correlation-guided fusion module to enhance the discriminative feature information of different ***,we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature *** conduct sufficient ablation experiments and comparative *** experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years,which can achieve better detection results.

关键词： Deepfake detection fine-grained classification multi-branch global attention

A New Childhood Pneumonia Diagnosis Method Based on fine-grained Convolutional Neural Network

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Computer Modeling in Engineering & Sciences 2022年第12期133卷 873-894页

作者： Yang Zhang Liru Qiu Yongkai Zhu Long Wen Xiaoping Luo School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan430074China Department of Pediatrics Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China

Pneumonia is part of the main diseases causing the death of *** is generally diagnosed through chest Xray *** the development of Deep Learning(DL),the diagnosis of pneumonia based on DL has received extensive ***,due to the small difference between pneumonia and normal images,the performance of DL methods could be *** research proposes a new fine-grained Convolutional Neural Network(CNN)for children’s pneumonia diagnosis(FG-CPD).Firstly,the fine-grainedCNNclassificationwhich can handle the slight difference in images is *** obtain the raw images from the real-world chest X-ray data,the YOLOv4 algorithm is trained to detect and position the chest part in the raw ***,a novel attention network is proposed,named SGNet,which integrates the spatial information and channel information of the images to locate the discriminative parts in the chest image for expanding the difference between pneumonia and normal ***,the automatic data augmentation method is adopted to increase the diversity of the images and avoid the overfitting of *** FG-CPD has been tested on the public Chest X-ray 2017 dataset,and the results show that it has achieved great ***,the FG-CPD is tested on the real chest X-ray images from children aged 3–12 years ago from Tongji *** results show that FG-CPD has achieved up to 96.91%accuracy,which can validate the potential of the FG-CPD.

关键词： Childhood pneumonia diagnosis fine-grained classification YOLOv4 attention network Convolutional Neural Network(CNN)

Neural Network Design Based on Differentiable Attention Module Search

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Neural Network Design Based on Differentiable Attention Modu...