58 results about "Max pooling" patented technology

The invention relates to a rapid target detection method based on a convolutional neural network, and relates to the computer vision technology. The rapid target detection method comprises the following steps: training convolutional neural network parameters by utilizing a training set; solving the problem of max-pooling losing feature by using an expander graph and generating a discriminative complete feature graph; regarding the full-connection weight of the convolutional neural network as a linear classifier, and estimating the generalization error of the linear classifier on the discriminative complete feature by using a probable approximately correct learning framework; estimating the required number of the linear classifiers according to the generalization error and the expected generalization error threshold value; and finally, completing the target detection on the discriminative complete feature graph by using the linear classifiers on the basis of a smooth window. The detection efficiency and the target detection precision are obviously improved.

An in vivo face detection method based on a convolutional neural network relates to the machine learning and mode identification field, and is provided to face a cheat problem in the face identification. A conventional face identification technology is very easy to attack, the attackers frequently copy the faces of the legal users by the methods, such as the photographs, the videos, the 3D models, etc., if a face identification system can not distinguish the real faces and the fake faces effectively, the invaders can pass the face identification system very easily by the fake identities. Based on the problem, the present invention provides the in vivo face detection method based on the convolutional neural network. The convolutional neural network in the method provided by the present invention is realized based on a cuda_convnet framework, the network structure comprises four convolutional layers of two max-pooling layers, one full connection layer and one soft_max layer, and the soft_max layer comprises two nerve cells used for predicting the probability distribution of the real and fake faces.

The invention provides a lightweight and efficient neural network model, and designs a multi-task scene semantic comprehension model based on the model. The neural network comprises the following fivestages that: stage one: adopting an mC.ReLU module; stage two: adopting Max-Pooling to carry out downsampling, and then, adopting a plurality of Block-B modules; stage three: adopting a plurality ofBlock-B modules and a plurality of Block-D modules; stage four: adopting a plurality of Block-A modules and a plurality of Block-C modules; and stage five: adopting a plurality of Block-A modules. Themulti-task scene semantic comprehension model comprises the neural network model, a multilevel fusion network and a multi-task semantic prediction network, wherein the neural network model and the multilevel fusion network share different tasks to serve as a common feature extractor. The model can be applied to the field of automatic driving scene perception, and is favorable for realizing road obstacle detection, scene semantic segmentation and real-time multi-task semantic prediction.

A method re-identifies objects in a pair of images by applying a convolutional neural network (CNN). Each layer in the network operates on an output of a previous layer. The layers include a first convolutional layer and a first max pooling layer to determine a feature map, a cross-input neighborhood differences layer to produce neighborhood difference maps, a patch summary layer to produce patch summary feature maps, a first fully connected layer to produce a feature vector representing higher order relationships in the patch summary feature maps, a second fully connected layer to produce two scores representing positive pair and negative pair classes, and a softmax layer to produce positive pair and negative pair probabilities. Then, the positive pair probability is output to signal whether the two images represent the same object or not.

Feature extraction, coding and pooling, are important components on many contemporary object recognition paradigms. This method explores pooling techniques that encode the second-order statistics of local descriptors inside a region. To achieve this effect, it introduces multiplicative second-order analogues of average and max pooling that together with appropriate non-linearities that lead to exceptional performance on free-form region recognition, without any type of feature coding. Instead of coding, it was found that enriching local descriptors with additional image information leads to large performance gains, especially in conjunction with the proposed pooling methodology. Thus, second-order pooling over free-form regions produces results superior to those of the winning systems in the Pascal VOC 2011 semantic segmentation challenge, with models that are 20,000 times faster.

An improved method of performing object segmentation and classification that reduces the memory required to perform these tasks, while increasing predictive accuracy. The improved method utilizes a capsule network with dynamic routing. Capsule networks allow for the preservation of information about the input by replacing max-pooling layers with convolutional strides and dynamic routing, allowing for the reconstruction of an input image from output capsule vectors. The present invention expands the use of capsule networks to the task of object segmentation and medical image-based cancer diagnosis for the first time in the literature; extends the idea of convolutional capsules with locally-connected routing and propose the concept of deconvolutional capsules; extends the masked reconstruction to reconstruct the positive input class; and proposes a capsule-based pooling operation for diagnosis. The convolutional-deconvolutional capsule network shows strong results for the tasks of object segmentation and classification with substantial decrease in parameter space.

The present invention provides a dense three-dimensional object reconstruction method based on learning efficient point cloud generation. The main content of the method comprises: an object reconstruction system, structure parameter setting and a training method. The process of the method comprises: an encoder is employed to extract an input image to an abstract representation space and perform union two-dimensional projection, a structure generator is employed to generate point cloud on the basis of two-dimensional convolution; up-sampling operation is employed to enlarge the image resolution, after the point cloud is projected to a two-dimensional plane, and the max-pooling operation is employed to recover the image to the original resolution to obtain a reconstruction object view. The dense three-dimensional object reconstruction method based on learning efficient point cloud generation can process three-dimensional views with different visual angles, and a pseudo compensation device is improved to solve the pixel superposition problem so as to improve the recognition rate of object reconstruction and the generation correct rate in a new perspective.

The invention discloses a method, for labeling object categories which pixels belong to, based on super pixel level features and belongs to the field of scene understanding and image semantic segmentation. Labeled training set images are used for training, and a to-be-labeled image is labeled by using the steps of firstly, extracting and encoding the features of each pixel; secondly, acquiring super pixels through 'over-segmentation; building different-level neighborhood areas for each super pixel according to different distances; for each area, using Max-Pooling comprehensive features to connect the features of different levels so as to obtain the level features of the super pixels; thirdly, using the level features to build and solve random field models based on the super pixels through linear SVM to obtain image pixel labels. By the method, pixel labelling time can be reduced, and labeling precision can be increased. In addition, the CUDA parallel calculation technology can be used during labeling so as to further increase labeling speed.

The invention provides a text sentiment classification method and a device, equipment and a storage medium. The method comprises the steps of obtaining a word vector in to-be-processed text data, andextracting a feature vector corresponding to the word vector; extracting context feature representation of the feature vector by adopting a bi-directional long-short term memory network Bi-LSTM model;according to the extracted context feature representation, utilizing an Attention mechanism, then introducing a top-k-max pooling processing mode to fully extract text feature representation, and theextracted features are sent to a classifier to obtain higher accuracy. According to the method, the classification accuracy of text sentiment classification is improved, and the classification effectis good.

A computer-implemented method, computer program product, and computer processing system are provided for word embedding. The method includes receiving, by a processor device, a word embedding matrix. The method further includes generating, by a processor device, an average pooling vector and a max pooling vector, based on the word embedding matrix. The method also includes generating, by the processor device, a prediction by applying a Multi-Layer Perceptron (MLP) to the average pooling vector and the max pooling vector.

The invention discloses a short text similarity calculation method based on multi-dimensional convolution characteristics, which comprises the following steps: constructing a multi-granularity convolution neural network model by using training data; Two training samples are inputted into the input layer of the multi-granularity convolution neural network model to obtain their word vector matrices.Multi-granularity convolution operation is carried out in the convolution layer to extract respective feature vectors. Using the K-Block-Max pooling and average pooling method in a pooling layer to extract quadratic eigenvectors. In the similarity calculation layer, the similarity vectors of the two training samples are obtained by using the fusion direction and distance calculation method. The similarity values of the two training samples are calculated in the whole connection layer and compared with the similarity values labeled in the training data to update the model. Two pieces of shorttext which need to be calculated similarity are input into the trained multi-granularity convolution neural network model, and the similarity value is output at all connection layers. The invention adopts different granularity convolution check short text data for feature extraction to improve accuracy.

The invention discloses an image classification method based on a field programmable gate array (FPGA). The method comprises the realization steps of (1), obtaining a test data set; (2), obtaining a training data set; (3), establishing a 15-layer convolutional neural network comprising 10 convolutional layers, 3 max-pooling layers, an average-pooling layer and a soft max layer; (4), setting kernelparameters of each layer in the convolutional neural network; (5), training the convolutional neural network through utilization of the training data set; (6), inputting test images into the convolutional neural network, and classifying the test data set; and (7), computing accuracy of the test data set. The method can be used for classifying optical images, SAR (Synthetic Aperture Radar) imagesand natural images on the field programmable gate array (FPGA).

An artificial neural network architecture is provided for processing raw audio waveforms to create speaker representations that are used for text-independent speaker verification and recognition. The artificial neural network architecture includes a strided convolution layer, first and second sequentially connected residual blocks, a transformer layer, and a final fully connected (FC) layer. The strided convolution layer is configured to receive raw audio waveforms from a speaker. The first and the second residual blocks both include multiple convolutional and max pooling layers. The transformer layer is configured to aggregate frame level embeddings to an utterance level embedding. The output of the FC layer creates a speaker representation for the speaker whose raw audio waveforms were inputted into the strided convolution layer.

The invention discloses a malicious software operation code analysis method based on a convolutional neural network. The method comprises the steps of: obtaining a Dalvik byte code; obtaining an operation code sequence, and representing the operation code sequence by a one-hot vector; converting the one-hot vector into a vector with a fixed size, multiplying the vector by a random weight matrix, and inputting the vector into a convolutional neural network; outputting a feature mapping set matrix C in the convolution layer; in k-max pooling, performing maximum merging operation on the matrix C,and extracting the most important k characteristic values to output a characteristic vector Z; forming a full connection layer by the vector Z, and operating the vector Z in the full connection layerto obtain an output feature y; processing the output feature y by using a softmax function to obtain relative probability distribution p; calculating a cross entropy loss function Lk; gradually adjusting the minimum loss function and the parameter values of the corresponding model by using a gradient descent method; iteratively updating model parameters based on the output calculations and optimizing the detection model. The method has the characteristic of high detection accuracy.

A method is provided for image processing. The method includes receiving an image dataset having image data for a plurality of images and a group dataset having group information for each image. The method further includes transforming the image dataset into a sparse vector by applying sparse coding and using the group information. The method also includes dividing each of dimensions of the sparse vector into multiple groups with a matrix. The matrix is generated such that the matrix takes on a higher value when samples in a same group from among the multiple groups have same non-zero dimensions above a threshold amount of time and takes on a tower value compared to the higher value when the samples in the same group lack the same non-zero coefficients above the threshold amount of time. The method additionally includes transforming the sparse vector into a summation vector using the multiple groups.

The invention discloses a SAR image classification method based on SAR-SIFT and DBN. The problem of synthetic aperture radar image classification is mainly solved. The classification process comprises the following steps: (1) inputting an integer type SAR image matrix; (2) transforming the SAR image matrix; (3) extracting SAR-SIFT features; (4) max pooling; (5) normalization; (6) training four restricted Boltzmann machines RBM; (7) training a softmax classifier; (8) building a deep belief network DBN; (9) classification; and (10) calculating the classification accuracy. According to the invention, the SAR-SIFT features of synthetic aperture radar images are extracted, the deep belief network DBN is adopted, the features are learned layer by layer, the information integrity of the radar images is retained, depth information is mined, and a good classification effect is achieved. The method can be used for SAR image classification.

In an optical character recognition (OCR) method for digitizing printed text images using a long-short term memory (LSTM) network, text images are pre-processed using a stroke-aware max-min pooling method before being fed into the network, for both network training and OCR prediction. During training, an average stroke thickness is computed from the training dataset. Stroke-aware max-min pooling is applied to each text line image, where minimum pooling is applied if the stroke thickness of the line is greater than the average stroke thickness, while max pooling is applied if the stroke thickness is less than or equal to the average stroke thickness. The pooled images are used for network training. During prediction, stroke-aware max-min pooling is applied to each input text line image, and the pooled image is fed to the trained LSTM network to perform character recognition.

The invention provides a system and method for completing a three-dimensional laser radar point cloud target. The global features of the laser radar target are obtained by three layers of shared multilayer perceptron and three point-by-point max-pooling layers, so that the object point cloud obtained by the laser radar through scanning is denser and more uniform in distribution, and the details ofthe contour of an object are more complete, so as to realize the purposes of detection recognition and measurement calculation.

The invention discloses a SAR image target recognition method based on a denoising self-coding network. The method comprises the following steps: denoising an image by using a three-dimensional modulematching algorithm (BM3D); for the original image and the denoised training image, the dense feature points are extracted by the sliding window and stored in pairs according to the corresponding positions, which are used for the input of the depth denoising encoder in the later training. Using the SIFT feature extracted from the denoised image as the original input x. The SIFT extracted from thecorresponding undenoised image is used as the noisy input to train the deep denoising self-coding network. The spatial pyramid model (SPM) is used to compute the image feature vector expression, and the maximum pooling (Max Pooling) is used to summarize the local features to obtain the final image description vector. A large number of dense SIFT features are used for deep network training, and thehigh-level representation of deep denoising and self-coding features is used for learning. Finally, the linear support vector machine is trained to classify and recognize the features instead of theinitial local features.

The present invention is suitable for the image classification, and provides a classification method of the hyperspectral images. The classification method comprises the steps of dividing the hyperspectral images into a training set and a test set, extracting the local feature points, using a K-means algorithm to calculate the local feature points of the training set to form a dictionary, adoptinga KNN algorithm to form the nearest neighbor words for the to-be-classified local feature points of the test set in the dictionary, searching the nearest neighbor feature points for the to-be-classified feature points of the test set images, searching the neighbor word having the shortest spectral dimension distance in the nearest neighbor words, introducing the triple constraints of the neighborfeature point, the neighbor words and the spectral dimension distance, solving a constraint least absolute fitting problem to obtain a coding coefficient, pooling the coding coefficient by a max-pooling algorithm, and classifying the test set by taking the obtained coding coefficient as a feature descriptor of the hyperspectral images. According to the present invention, an indeterminacy problemwhen a mapping relationship of the hyperspectral image feature points and the dictionary words is established is solved,and the discernment of the similar images is improved.

The invention discloses a decision tree classification foreign Chinese difficulty evaluation method based on svm and bert model random subspace feature selection. The method comprises the steps: generating 86 statistical features according to the length and readability of an article, carrying out the classification through svm, and obtaining a confidence degree 1; and classifying the encoding features by using svm to obtain a confidence coefficient 2. And fusing the obtained two confidence coefficients to serve as new features, and classifying the features by using a decision tree. And for theencoding feature data, outputting an information result through the-1 layer of the encoding extracted by the BERT model, and then performing average-> max pooling processing to obtain 768-dimensionalfeatures in total without normalization. According to the method, the problems of low efficiency and under-fitting of a traditional algorithm are avoided, and all information is used most reasonably,so that the classification basis is increased, and the effect is remarkable. According to the method, the accuracy of evaluating the difficulty of foreign Chinese is 85.6%.

The invention provides a method, device and system for detecting a pedestrian detection misinformation result and a storage medium. The method comprises the steps of: acquiring a pedestrian detectionresult obtained after pedestrian detection is carried out for to-be-processed data; and detecting whether the pedestrian detection result is a misinformation result by utilizing a trained pedestrian misinformation judgment network, wherein the pedestrian misinformation judgment network comprises a convolutional layer, a max-pooling layer, at least one convolutional block unit, a global pooling layer and an output layer which are sequentially connected. According to the method, device and system for detecting the pedestrian detection misinformation result and the storage medium, which are provided by the embodiment of the invention, the pedestrian misinformation judgment network with a specific network structure is utilized to implement misinformation detection of the pedestrian detection result, accuracy of pedestrian misinformation detection can be greatly promoted and a calculated amount cannot be substantially increased, so that usability of a pedestrian detection application product can be greatly promoted.