Deep Convolutional Neural Networks (DCNNs) can well extract the features from natural images. However, the classification functions in the existing network architecture of CNNs are simple and lack capabilities to handle important spatial information as have been done by many well-known traditional ...
Real-world face recognition requires us to perceive the uniqueness of a face across variable images. Deep convolutional neural networks (DCNNs) accomplish this feat by generating robust face representations that can be analysed in a multidimensional ‘face space’. We examined the organization of view...
Deep convolutional neural networks(DCNNs)have shown outstanding performance in the fields of computer vision,natural language processing,and complex system analysis.With the improvement of performance with deeper layers,DCNNs incur higher computational complexity and larger storage requirement,making it ...
To overcome these challenges, a Deep Convolutional Neural Network Object Net (DCNNONet) model is proposed. The adaptive non local moment mean filter enhances image preprocessing, optimizing noise suppression and texture preservation. Utilizing proposed model, which includes featuring EfficientNetB7, a ...
Deep convolutional neural networks (DCNNs) are a holistic approach that recently enabled a quantum leap in the field. In 2014, Facebook reported a face recognition system named DeepFace [27] which achieved near-human performance on LFW benchmark [31]. This accuracy was quickly surpassed by syst...
Deep convolutional neural networks (DCNNs) have been successfully used in many computer vision tasks. Previous works on DCNN acceleration usually use a fixed computation pattern for diverse DCNN models, leading to imbalance between power efficiency and performance. We solve this problem by designing a...
DeepLab的优势:(1)空洞卷积提高了速度(2)准确率:在VOC的多个任务上实现state-of-art(3)简约性:DCNNs+CRFs DeepLabv2相比DeepLabv1的改进:对多尺寸的图片分割效果更好,引入ASPP,用ResNet作为backbone,实现比VGG16更好的效果。 相关工作 先前主要靠将手工设计的特征与boosting,随机森林,SVM等分类器结合实现较好的...
Abstract Recent years have witnessed the great progress for speech emotion recognition using deep convolutional neural networks (DCNNs). In order to improve the performance of speech emotion recognition, a novel feature fusion method is proposed. With going deeper of the convolutional layers, the conv...
Deep Convolutional Neural Networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rec...
Deep Convolutional Neural Networks (DCNNs) present a machine learning tool that enables the computer to learn from image samples and extract internal representations or properties underlying grouping or categories of the images. DCNNs have been used successfully for image classification, object ...