Generative adversarial networks(GANs) have become a competitive method among computer vision tasks. There have been many studies devoted to utilizing generative network to do generative tasks, such as images synthesis...Generative adversarial networks(GANs) have become a competitive method among computer vision tasks. There have been many studies devoted to utilizing generative network to do generative tasks, such as images synthesis. In this paper, a semi-supervised learning scheme is incorporated with generative adversarial network on image classification tasks to improve the image classification accuracy. Two applications of GANs are mainly focused on: semi-supervised learning and generation of images which can be as real as possible. The whole process is divided into two sections. First, only a small part of the dataset is utilized as labeled training data. And then a huge amount of samples generated from the generator is added into the training samples to improve the generalization of the discriminator. Through the semi-supervised learning scheme, full use of the unlabeled data is made which may contain potential information. Thus, the classification accuracy of the discriminator can be improved. Experimental results demonstrate the improvement of the classification accuracy of discriminator among different datasets, such as MNIST, CIFAR-10.展开更多
Fruit classification is found to be one of the rising fields in computer and machine vision.Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues.The performance of th...Fruit classification is found to be one of the rising fields in computer and machine vision.Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues.The performance of the classification scheme depends on the range of captured images,the volume of features,types of characters,choice of features from extracted features,and type of classifiers used.This paper aims to propose a novel deep learning approach consisting of Convolution Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-TermMemory(LSTM)application to classify the fruit images.Classification accuracy depends on the extracted and selected optimal features.Deep learning applications CNN,RNN,and LSTM were collectively involved to classify the fruits.CNN is used to extract the image features.RNN is used to select the extracted optimal features and LSTM is used to classify the fruits based on extracted and selected images features by CNN and RNN.Empirical study shows the supremacy of proposed over existing Support Vector Machine(SVM),Feed-forwardNeural Network(FFNN),and Adaptive Neuro-Fuzzy Inference System(ANFIS)competitive techniques for fruit images classification.The accuracy rate of the proposed approach is quite better than the SVM,FFNN,and ANFIS schemes.It has been concluded that the proposed technique outperforms existing schemes.展开更多
针对基于关系边界框提取的谓词特征粒度相对较粗的问题,提出区域敏感的场景图生成(region-sensitive scene graph generation,RS-SGG)方法。谓词特征提取模块将关系边界框分为4个区域,基于自注意力机制抑制关系边界框中与关系分类无关...针对基于关系边界框提取的谓词特征粒度相对较粗的问题,提出区域敏感的场景图生成(region-sensitive scene graph generation,RS-SGG)方法。谓词特征提取模块将关系边界框分为4个区域,基于自注意力机制抑制关系边界框中与关系分类无关的背景区域。关系特征解码器在进行关系预测时不仅考虑了物体对的视觉特征和语义特征,也考虑了物体对的位置特征。在视觉基因组(visual genome,VG)数据集上分别计算了RS-SGG方法针对场景图生成、场景图分类和谓词分类3个子任务的图约束召回率和无图约束召回率,并与主流的场景图生成方法进行了比较。实验结果表明,RS-SGG的图约束召回率和无图约束召回率均优于主流方法。此外,可视化实验结果也进一步证明了所提出方法的有效性。展开更多
Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and operated.Most onboard advanced Io T devices are equipped with visual sensors that form the so-c...Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and operated.Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T.Typically,the sender would compress images,and then through the communication network,the receiver would decode images,and then analyze the images for applications.However,image compression and semantic inference are generally conducted separately,and thus,current compression algorithms cannot be transplanted for the use of semantic inference directly.A collaborative image compression and classification framework for visual Io T applications is proposed,which combines image compression with semantic inference by using multi-task learning.In particular,the multi-task Generative Adversarial Networks(GANs)are described,which include encoder,quantizer,generator,discriminator,and classifier to conduct simultaneously image compression and classification.The key to the proposed framework is the quantized latent representation used for compression and classification.GANs with perceptual quality can achieve low bitrate compression and reduce the amount of data transmitted.In addition,the design in which two tasks share the same feature can greatly reduce computing resources,which is especially applicable for environments with extremely limited resources.Using extensive experiments,the collaborative compression and classification framework is effective and useful for visual IoT applications.展开更多
Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning dis...Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zeroshot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot finegrained classification.展开更多
针对现有算法对于皮肤病变数据集形态复杂、各类样本不平衡导致分类精度低、特征提取能力不强等问题,提出了一种基于皮肤病变图像的风格对抗生成网络与设计的ECA-ConvNext分类网络结合的皮肤病变图像生成分类方法(SL-style-GAN2 and ECA...针对现有算法对于皮肤病变数据集形态复杂、各类样本不平衡导致分类精度低、特征提取能力不强等问题,提出了一种基于皮肤病变图像的风格对抗生成网络与设计的ECA-ConvNext分类网络结合的皮肤病变图像生成分类方法(SL-style-GAN2 and ECA-ConvNeXt Frame,SAE)。首先,对风格对抗生成网络中对生成器重新设计,并且对判别器部分进行重构,使判别器可以同时为生成器提供局部和全局信息,从而生成更好的样本图片以供后续分类模型得到更好的效果。之后选用ConvNeXt-T为分类基础网络,设计了深层特征提取模块(Deep information extraction module,DIEM)使通道和权值之间直接联系,提高网络特征提取能力,从而提高模型精度。最后,在ISIC 2018数据集上进行实验,实验结果表明,分类准确率达到94.0%,比原始ConvNeXt提高了4.5%。展开更多
基金Supported by the National Natural Science Foundation of China(No.61501457)National Key Technology R&D Program(No.2015BAK21B00)
文摘Generative adversarial networks(GANs) have become a competitive method among computer vision tasks. There have been many studies devoted to utilizing generative network to do generative tasks, such as images synthesis. In this paper, a semi-supervised learning scheme is incorporated with generative adversarial network on image classification tasks to improve the image classification accuracy. Two applications of GANs are mainly focused on: semi-supervised learning and generation of images which can be as real as possible. The whole process is divided into two sections. First, only a small part of the dataset is utilized as labeled training data. And then a huge amount of samples generated from the generator is added into the training samples to improve the generalization of the discriminator. Through the semi-supervised learning scheme, full use of the unlabeled data is made which may contain potential information. Thus, the classification accuracy of the discriminator can be improved. Experimental results demonstrate the improvement of the classification accuracy of discriminator among different datasets, such as MNIST, CIFAR-10.
基金This research is funded by Taif University,TURSP-2020/150.
文摘Fruit classification is found to be one of the rising fields in computer and machine vision.Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues.The performance of the classification scheme depends on the range of captured images,the volume of features,types of characters,choice of features from extracted features,and type of classifiers used.This paper aims to propose a novel deep learning approach consisting of Convolution Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-TermMemory(LSTM)application to classify the fruit images.Classification accuracy depends on the extracted and selected optimal features.Deep learning applications CNN,RNN,and LSTM were collectively involved to classify the fruits.CNN is used to extract the image features.RNN is used to select the extracted optimal features and LSTM is used to classify the fruits based on extracted and selected images features by CNN and RNN.Empirical study shows the supremacy of proposed over existing Support Vector Machine(SVM),Feed-forwardNeural Network(FFNN),and Adaptive Neuro-Fuzzy Inference System(ANFIS)competitive techniques for fruit images classification.The accuracy rate of the proposed approach is quite better than the SVM,FFNN,and ANFIS schemes.It has been concluded that the proposed technique outperforms existing schemes.
文摘针对基于关系边界框提取的谓词特征粒度相对较粗的问题,提出区域敏感的场景图生成(region-sensitive scene graph generation,RS-SGG)方法。谓词特征提取模块将关系边界框分为4个区域,基于自注意力机制抑制关系边界框中与关系分类无关的背景区域。关系特征解码器在进行关系预测时不仅考虑了物体对的视觉特征和语义特征,也考虑了物体对的位置特征。在视觉基因组(visual genome,VG)数据集上分别计算了RS-SGG方法针对场景图生成、场景图分类和谓词分类3个子任务的图约束召回率和无图约束召回率,并与主流的场景图生成方法进行了比较。实验结果表明,RS-SGG的图约束召回率和无图约束召回率均优于主流方法。此外,可视化实验结果也进一步证明了所提出方法的有效性。
基金supported by the National Key R&D Program of China(No.:2019YFB1803400)the National Natural Science Foundation of China(Nos.NSFC 61925105,61801260 and U1633121)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.FRF-NP-2003)supported by Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute。
文摘Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and operated.Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T.Typically,the sender would compress images,and then through the communication network,the receiver would decode images,and then analyze the images for applications.However,image compression and semantic inference are generally conducted separately,and thus,current compression algorithms cannot be transplanted for the use of semantic inference directly.A collaborative image compression and classification framework for visual Io T applications is proposed,which combines image compression with semantic inference by using multi-task learning.In particular,the multi-task Generative Adversarial Networks(GANs)are described,which include encoder,quantizer,generator,discriminator,and classifier to conduct simultaneously image compression and classification.The key to the proposed framework is the quantized latent representation used for compression and classification.GANs with perceptual quality can achieve low bitrate compression and reduce the amount of data transmitted.In addition,the design in which two tasks share the same feature can greatly reduce computing resources,which is especially applicable for environments with extremely limited resources.Using extensive experiments,the collaborative compression and classification framework is effective and useful for visual IoT applications.
文摘高光谱遥感影像智能解译是实现高光谱遥感应用的重要研究任务之一。针对生成式对抗网络在高光谱遥感影像分类中空谱特征利用不足的问题,提出了一种基于CVAE-GAN的高光谱遥感影像分类对抗网络算法(hyperspectral remote sensing classification based on CVAE-CGAN,HCVAE-CGAN),通过搭建1D-CNN分类模型和2D-CNN分类模型,训练判别器识别空谱特征,利用CVAE替代生成器结构生成影像光谱特征和空间特征,通过encode模块处理训练集得到空谱特征值,并将空谱特征值解码生成图像光谱,随后比对原始图像进行decode网络模型的优化,最后利用生成样本对分类器进行训练。实验结果表明,HCVAE-CGAN方法在小样本训练中有更好的检测性能,在Indian Pines和Pavia University数据集中的总体精度分别提高了2.85个百分点和3.92个百分点。
基金supported by National Basic Research Program of China (973 Program) (No. 2015CB352502)National Nature Science Foundation of China (No. 61573026)Beijing Nature Science Foundation (No. L172037)
文摘Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zeroshot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot finegrained classification.
文摘针对现有算法对于皮肤病变数据集形态复杂、各类样本不平衡导致分类精度低、特征提取能力不强等问题,提出了一种基于皮肤病变图像的风格对抗生成网络与设计的ECA-ConvNext分类网络结合的皮肤病变图像生成分类方法(SL-style-GAN2 and ECA-ConvNeXt Frame,SAE)。首先,对风格对抗生成网络中对生成器重新设计,并且对判别器部分进行重构,使判别器可以同时为生成器提供局部和全局信息,从而生成更好的样本图片以供后续分类模型得到更好的效果。之后选用ConvNeXt-T为分类基础网络,设计了深层特征提取模块(Deep information extraction module,DIEM)使通道和权值之间直接联系,提高网络特征提取能力,从而提高模型精度。最后,在ISIC 2018数据集上进行实验,实验结果表明,分类准确率达到94.0%,比原始ConvNeXt提高了4.5%。
