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1172results about How to "Reduce the amount of parameters" patented technology

Remote sensing image semantic segmentation method based on attention multi-scale feature fusion

The invention discloses a remote sensing image semantic segmentation method based on attention multi-scale feature fusion, and the method comprises the steps: establishing a semantic segmentation network based on attention multi-scale feature fusion, building a training data set, and carrying out the network parameter training through employing the training data set. And performing semantic segmentation on to-be-tested data by using the trained network during testing. And the network is a lightweight encoder-decoder structure. Wherein the idea of an image cascade network is introduced; meanwhile, coding features and decoding features are optimized by using an attention mechanism, a multi-scale attention optimization module, a multi-scale feature fusion module and a boundary enhancement module are constructed, feature maps of different scales are extracted and fused, training is guided by using multi-scale semantic tags and boundary tags, and semantic segmentation of remote sensing images can be effectively carried out.
Owner:CHINA UNIV OF MINING & TECH

Multi-scale image semantic segmentation method

The invention discloses a multi-scale image semantic segmentation method. The method comprises the following steps: obtaining a to-be-segmented image and a corresponding label; constructing a full convolutional deep neural network, wherein the full convolutional deep neural network comprises a convolution module, a hole convolution module, a pyramid pooling module, a 1 * 1 * depth convolution layer and a deconvolution structure; setting hole convolution as channel-by-channel operation, and utilizing low-scale, medium-scale and high-scale characteristics in a targeted mode; training the full convolutional deep neural network, establishing a loss function, and determining parameters of the full convolutional deep neural network by training the sample image; and inputting the to-be-segmentedimage into the trained full convolutional deep neural network to obtain a semantic segmentation result. By means of the method, the image semantic segmentation problem with complex details, holes andlarge targets can be well solved while the calculated amount and the parameter number are reduced, and the consistency of category labels can be reserved while the target edges can be well segmented.
Owner:SOUTH CHINA UNIV OF TECH

Full convolution neural network (FCN)-based monocular image depth estimation method

The invention discloses a full convolution neural network (FCN)-based monocular image depth estimation method. The method comprises the steps of acquiring training image data; inputting the training image data into a full convolution neural network (FCN), and sequentially outputting through pooling layers to obtain a characteristic image; subjecting each characteristic image outputted by a last pooling layer sequentially to amplification treatment to obtain a new characteristic image the same with the dimension of a characteristic image outputted by a previous pooling layer, and fusing the twocharacteristic images; sequentially fusing the outputted characteristic image of each pooling layer from back to front so as to obtain a final prediction depth image; training the parameters of the full convolution neural network (FCN) by utilizing a random gradient descent method (SGD) during training; acquiring an RGB image required for depth prediction, and inputting the RGB image into the well trained full convolution neural network (FCN) so as to obtain a corresponding prediction depth image. According to the method, the problem that the resolution of an output image is low in the convolution process can be solved. By adopting the form of the full convolution neural network, a full-connection layer is removed. The number of parameters in the network is effectively reduced.
Owner:NANJING UNIV OF POSTS & TELECOMM

Lightweight semantic segmentation method for high-resolution remote sensing image

ActiveCN112183360ASolve the inefficiency of operationRun fastScene recognitionComputation complexityEncoder decoder
A lightweight semantic segmentation method for a high-resolution remote sensing image comprises the steps of network construction, training and testing. Specifically, a deep semantic segmentation network of an encoder-decoder structure is constructed for a pytorch deep learning framework, after network training is carried out based on a remote sensing image data sample set, a to-be-tested remote sensing image serves as network input. A segmentation result of the remote sensing image is obtained. According to the method, on one hand, model parameters are reduced by decomposing depth separable convolution, the calculation complexity is reduced, the semantic segmentation time of the high-resolution remote sensing image is shortened, and the semantic segmentation efficiency of the high-resolution remote sensing image is improved; and on the other hand, semantic segmentation precision is improved through multi-scale feature aggregation, a spatial attention module and gating convolution, sothat the proposed lightweight deep semantic segmentation network can accurately and efficiently realize semantic segmentation of a high-resolution remote sensing image.
Owner:SHANGHAI JIAO TONG UNIV

A face multi-area fusion expression recognition method based on depth learning

The invention discloses a face multi-area fusion expression recognition method based on depth learning, which comprises the following steps of detecting a face position with a detection model; obtaining the coordinates of the key points by using the key point model; aligning the eyes according to the key points of the eyes, then aligning the face according to the coordinates of the key points of the whole face, and clipping the face region by affine transformation; cutting the eye and mouth areas of the image to a certain proportion; dividing the convolution neural network into one backbone network and two branch networks; carrying out the feature fusion in the last convolution layer, and finally obtaining the expression classification results by the classifier. The method of the inventionutilizes the priori information, besides the whole face, the eyes and mouth regions are also used as the input of the network, and the network can learn the whole semantic features of facial expressions and the local features of facial expressions through model fusion, so that the method simplifies the difficulty of facial expression recognition, reduces the external noise, and has strong robustness, high accuracy, low complexity of the algorithm and so on.
Owner:SOUTH CHINA UNIV OF TECH

Traffic image multi-type vehicle detection method based on deep study

The invention discloses a traffic image multi-type vehicle detection method based on deep study. The traffic image multi-type vehicle detection method based on deep study firstly combines a nerve network characteristic with a region generation algorithm, realizes two processes-region generation and region determination-at the same time through using a nerve network conventional layer, uses a background model to determine a motion area of a discrete series image targeting a specific scene and provides an extra reference basis to region generation, and combines with a vehicle detection result to perform upgrading and correction on the background model according to different conditions. Besides, the traffic image multi-type vehicle detection method also brings forward a network model compression scheme to reduce model parameters and calculation time, brings forward a new detection result optimization means based on grouped error calculation to replace a conventional non-maximum-value inhibition scheme, and improves overall detection accuracy.
Owner:安徽国联信息科技有限公司

Medical image segmentation method based on improved convolutional neural network

The invention provides a medical image segmentation method based on an improved convolutional neural network, and belongs to the field of image processing. According to the method, an improved convolutional neural network is constructed, and a multi-channel fusion module, a multi-channel dense connection module, a down-sampling module and an up-sampling module are designed on the basis of an original framework of a standard U-Net network. The depth of the network is increased while the network training calculation amount is controlled, and redundant calculation is reduced. Gradient disappearance is avoided by improving the internal structure of the network while the network is deepened. The experimental result is that the segmentation DICE coefficient of the output prediction graph of thetrained neural network is 98.57%, the DICE coefficient segmented by the original network is 98.26%, and the effectiveness of the method is reflected.
Owner:NORTHEASTERN UNIV

Voice wake-up method and device based on artificial intelligence, and computer equipment

The invention provides a voice wake-up method and device based on artificial intelligence, and computer equipment. The method comprises steps that windowing enframing operation of online recorded voice is carried out to acquire at least one voice frame; characteristic extraction of the voice frame is carried out to acquire voice characteristics; a voice wake-up model based on the convolutional neural network is utilized to carry out calculation of the static voice characteristics contained in the voice frame, and posterior probabilities of the static voice characteristics contained in the voice frame corresponding to a non-wake-up word class and a wake-up word class are acquired; if the posterior probability of the static voice characteristics contained in the voice frame corresponding to the wake-up word class is greater than a pre-determined threshold, the online recorded voice is determined to contain wake-up words. The method is advantaged in that parameter quantities of the voice wake-up model based on the convolutional neural model can be effectively reduced, so the operand reduction effect is realized, and the voice wake-up effect can be improved.
Owner:BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD

Fast pedestrian detection method and device

The invention discloses a fast pedestrian detection method and device. The method comprises the following steps: step S1, constructing a configurable deep model based on a convolutional neural network, and utilizing training samples to obtain parameters of a constructed network by learning to obtain a model used for a test process; and step S2, inputting test samples, utilizing a variation law ofneural-network perception domains, using different intermediate layers to detect target objects in different scale ranges through the trained model, and obtaining box graphs of the target objects in images by prediction. The method uses the different intermediate layers to detect the target objects in the certain scale ranges through utilizing the variation law of the neural-network perception domains, better adapts to relationships of the perception domains and object sizes, and effectively improves a detection result.
Owner:SUN YAT SEN UNIV

Method for detecting and identifying continuous segmented texts in image

The invention discloses a method for detecting and identifying continuous segmented texts in an image based on SegLink and Attention-based CRNN fusion processing, and belongs to the technical field ofoptical character recognition, aiming to solve the problems of low text detection accuracy, particularly low inclined text detection accuracy, difficulty in positioning, difficulty in font segmentation and low recognition accuracy in OCR information document digitization. The method includes the steps: establishing a SegLink + CRNN model based on a Tensorflow deep learning framework, detecting text lines in an image through a SegLink network; segmenting the segmented text according to lines; extracting single-line text features through a densely connected convolutional neural network; processing the sequence information of the context in the text by the bidirectional recurrent neural network, and adopting the CTC decoding algorithm to avoid the problem of single word segmentation, and eliminate the influence of the single word segmentation link on the recognition accuracy; and further fusing an Attention mechanism during CTC transcription to improve the recognition accuracy for the text sequence characteristics. The method is applicable to printed form and handwritten form recognition, and can be applied to recognition of multilingual texts such as English and Chinese.
Owner:SHANGHAI MARITIME UNIVERSITY

Improved deep convolutional neural network-based remote sensing image classification model

The invention relates to an improved deep convolutional neural network-based remote sensing image classification model. The model comprises the following steps of: S1, carrying out dimensionality reduction on a remote sensing feature image on the basis of a bottleneck unit; S2, carrying out convolutional multichannel optimization on the remote sensing feature image on the basis of grouped convolution; S3, improving feature extraction ability of the remote sensing feature image on the basis of channel shuffling; and S4, carrying out band processing on spatial position features of the remote sensing image. The model has the advantages that the dimensionality reduction of to-be-input remote sensing images is realized, and the convolutional calculation amount during the training of deep convolutional neural network-based remote sensing image classification model is reduced; a channel shuffling structure is constructed in allusion to spatial correlation of the remote sensing images, so thatthe feature extraction ability of a neural network in the grouped convolution stage is enhanced; and aiming at spatial position features of the remote sensing images, spatial position feature recognition degrees, for the remote sensing images, of the deep convolutional neural network-based model are improved.
Owner:SHANGHAI OCEAN UNIV

An efficient super-resolution method based on a deep back projection network

The invention discloses an efficient super-resolution method based on a deep back projection network. The method comprises the following steps: (1) obtaining a total training set and a test set; (2) preprocessing the total training set to complete data enhancement; (3) scaling the images in the total training set at different scales; and (4) image super-resolution reconstruction is realized basedon the convolutional neural network, and the convolutional neural network totally comprises 27 convolutional layers and specifically comprises three parts of feature extraction, error back projectionand image reconstruction. The combination of the group convolution and the 1 * 1 convolution is used for replacing the traditional convolution to redesign the iteration submodule, and the strategy caneffectively reduce the model parameter quantity and improve the model efficiency; Each iteration sub-module comprises an error feedback mechanism, so that error correction can be carried out in time;in addition, a channel weighting module is introduced, so that the model efficiency can be further improved.
Owner:TIANJIN UNIV

Embedded platform real-time tumble detection method based on improved attitude estimation algorithm

The invention relates to an embedded platform real-time tumble detection method based on an improved attitude estimation algorithm. According to the method, an attitude estimation network is established by using depth separable convolution, an attention mechanism and an inverse residual error structure and is used for fall detection. The precision of the attitude estimation network is further improved; the parameter quantity and the calculation quantity are greatly reduced; the distance of each articulation point of the human body between different video frames is calculated to track the humanbody; acceleration of human body articulation points is calculated by using front and rear video frames, and whether falling occurs is judged according to the acceleration, the relative positions ofthe articulation points and the like, so that the attitude estimation network is more suitable for being deployed on an embedded platform, and a real-time effect can be achieved by deploying on a TX2embedded platform. According to the method, human body tracking is carried out by using multi-person human body joint point coordinates and skeleton information obtained in front and back frames, posture estimation is more stable due to multi-person tracking, and the problem of fall detection in a multi-person scene can be better solved.
Owner:HEBEI UNIV OF TECH

remote sensing image rapid target detection method based on a deep Hash auxiliary network

The invention discloses a remote sensing image rapid target detection method based on a deep Hash auxiliary network, and solves the problem that an existing target detection framework cannot carry outefficient detection on a background area which does not have a target and occupies a large area in a wide remote sensing image. The method comprises the following implementation steps: generating anexperimental training image block set and a test image block set; constructing a remote sensing image rapid target detection model based on a deep Hash auxiliary network; training a remote sensing image rapid target detection model based on a deep Hash auxiliary network; and using the trained deep Hash auxiliary network to obtain a test result of the test image block set, and counting the runningtime. According to the method, the Hash auxiliary branch network module is introduced, redundant calculation in target detection is greatly reduced by quickly filtering most non-target background areas in the wide remote sensing image, the detection time of the non-target background areas is greatly shortened, and the detection efficiency is greatly improved. The method is used for target detection of wide remote sensing images.
Owner:XIDIAN UNIV

Garbage can capable of automatic classifying based on visual recognition and classifying method

The invention discloses a garbage can capable of automatic classifying based on visual identification and a classifying method. The garbage can comprises a garbage throwing opening, a first photoelectric switch sensor, an identifying and classifying tray, second photoelectric switch sensors, sub garbage cans, an image identification component, an STM32 controller, a double-path stepping motor driver and a garbage can shell, wherein the garbage throwing opening is formed in the side wall of the garbage can shell; the first photoelectric switch sensor is arranged in the garbage throwing opening;a plurality of sub garbage cans are arranged in the garbage can shell; a second photoelectric switch sensor is mounted in a can opening position of each sub garbage can; the identifying and classifying tray is arranged in the garbage can shell and is located above the sub garbage cans; the identifying and classifying tray comprises a garbage tray, a V-shaped baffle stepping motor, a camera, a V-shaped baffle, a support frame, a rotary baffle stepping motor and a rotary baffle. According to the garbage can, the camera is used for collecting garbage images; a TensorFlow deep learning frameworkis adopted; through transfer training of a model, the accuracy rate of garbage identification is increased.
Owner:石家庄邮电职业技术学院

Multi-branch target detection method based on traffic scene

The invention requests to protect a multi-branch target detection method based on a traffic scene, and the method comprises the steps: S1, obtaining a high-definition picture taken by a traffic intersection to construct a related data set, carrying out the classification and marking of traffic scene images, generating a corresponding category label, and dividing a training set and a test set; s2,building a network model with 32 layers based on deep learning, obtaining nine anchor frame priori through a k-means clustering algorithm; averagely distributing the nine anchor frames into three detection branches; enabling the network to convert the detection task into a regression task; simultaneously completing the classification of the targets and the regression of the bounding boxes on one network; unifying four steps of candidate box generation, feature extraction, classification and position finishing of a target detection algorithm into a deep network framework, carrying out end-to-end training on a network model by adopting a back propagation and random gradient descent method, reducing a loss function to a small range through iterative training, and then stopping training.
Owner:CHONGQING UNIV OF POSTS & TELECOMM

Vector distribution control method for torque of distributed-driven electric automobile

The invention discloses a vector distribution control method for torque of a distributed-driven electric automobile. Through the relation between vehicle driving state stability and the expected yaw velocity under a vehicle dynamical model, an ideal motion state of a vehicle under generalized additional yawing moment is worked out, and control stability of a system is judged and analyzed through the expected yaw velocity so as to determine whether yawing moment control is required or not; a tyre longitudinal slip rate is set as a specific value in a stable state, and driving torque is precisely distributed under the condition that a coefficient of road adhesion is met. Through reasonable distribution of driving or braking torque of front and back axles, the response speed to the expected yaw velocity can be remarkably increased, so that the vehicle has an ideal motion state when passing a curve, the problem of difficult steering of the vehicle when acceleration is not enough is effectively restrained, curve passing efficiency is improved, vehicle driving stability and smoothness are improved, control burden of the driver is remarkably reduced, and driving safety is improved.
Owner:SHANGHAI MUNICIPAL ELECTRIC POWER CO

Feature pyramid-based remote-sensing image time-sensitive target recognition system and method

The invention discloses a feature pyramid-based remote-sensing image time-sensitive target recognition system and method. The system comprises a target feature extraction sub-network, a feature layersub-network, a candidate area generation sub-network and a classification and regression sub-network, wherein the target feature extraction sub-network is used for carrying out multiple layers of convolution processing on a to-be-processed image and outputting the convolution processing result of each layer as a feature layer; the feature layer sub-network is used for overlapped the last feature layer and the current feature layer to obtain the current fused feature layer, wherein the topmost fused feature layer is a topmost feature layer; the candidate area generation sub-network is used forextracting candidate areas from different layers of fused feature layers; and the classification and regression sub-network is used for mapping the candidate areas to different layers of fused featurelayers so as to obtain a plurality of mapped fused feature layers, and carrying out target judgement on the plurality of mapped fused feature layers so as to output a result. According to the systemand method, the hierarchical structures of feature pyramids are utilized to ensure that all the scales of features have rich semantic information.
Owner:HUAZHONG UNIV OF SCI & TECH

Image content question and answer method based on multi-modality low-rank dual-linear pooling

The invention discloses an image content question and answer method based on multi-modality low-rank dual-linear pooling. The image content question and answer method comprises the following steps of 1, conducting data preprocessing on an answer text of a question described with an image and a natural language; 2, conducting characteristic fusing on a multi-modality low-rank dual-linear pooling model; 3, establishing a neural network structure based on an MFB pooling model and a collaborative concern model; 4, training the models and utilizing a backward propagation algorithm to train neural network parameters. The neural network model aiming at an image question & answer is provided, and especially a method for conducting unified modeling on cross-media data in a question-answer in the field of the image question and answer and a network structure for studying the 'collaborative concern' to conduct modeling description on an image and question fine-grained characteristics are provided, and the best effect currently in the field of the image question and answer is obtained.
Owner:HANGZHOU DIANZI UNIV

An online classroom atmosphere assessment system and method

The invention provides an online classroom atmosphere evaluation system and method. The evaluation system comprises a video stream collection module, a data stream processing module, an image analysismodule, a classroom attendance analysis module, a classroom atmosphere evaluation module, a classroom atmosphere scoring module and a display module; . The invention collects classroom video stream data through a camera, and the captured video is captured frame by frame to obtain images; all the faces images are segmented and numbered sequentially, at the same time, the eigenvalues are assigned to the faces with the corresponding numbers, and then face recognition and facial expression recognition are carried out according to the number, so as to identify the number, emotion and movement posture of students in the video stream data; the score 0 is given to the students lowering heads in the image and the score 1 is given to students having interaction; according to the emotional analysisstrategy of students listening to the teacher, the current student listening student score is obtained and finally comprehensive evaluation of the classroom atmosphere score is obtained. The inventioncan evaluate the classroom quality on-line and in real time, and can effectively improve the evaluation effect.
Owner:CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY

Image super-resolution reconstruction method

The invention relates to an image super-resolution reconstruction method, belongs to the image processing technology field and solves problems that the edge information of an image generated in the prior art is fuzzy, application to multiple magnification times cannot be realized and the reconstruction effect is poor. The method comprises steps that a convolutional neural network for training andlearning is constructed, and the convolutional neural network comprises an LR characteristic extraction layer, a nonlinear mapping layer and an HR reconstruction layer in order from top to bottom; inputted paired LR images and HR images are trained through utilizing the convolutional neural network, training of at least two magnification scales is performed simultaneously, and an optimal parameterset of the convolutional neural network and scale adjustment factors at the corresponding magnification scales are acquired; after the training is completed, the target LR images and the target magnification times are inputted to the convolutional neural network, and the target HR images are acquired. The method is advantaged in that the training speed of the convolutional neural network is fast,after training is completed, and the HR images at any magnification times in the training scale can be acquired in real time.
Owner:CHINA UNIV OF MINING & TECH

Face image restoration method based on multi-discriminator generative adversarial network

The invention discloses a face image restoration method based on a multi-discriminator generative adversarial network, and the method comprises the following steps: (1) carrying out the preprocessingof images in a disclosed face image database, and inputting the images into a generator, so as to obtain a generated image; (2) inputting the real image and the generated image into a plurality of discriminators to obtain a feedback value; (3) taking the feedback values of the plurality of discriminators as confrontation loss, and performing confrontation training on the generative adversarial network by combining the perception loss and the reconstruction loss; and (4) inputting the missing face image into the trained generator to obtain a repaired face image. Aiming at the problem of repairing the shielded or damaged face image, a generative adversarial network structure with multiple discriminators is adopted, so that the problem of low authenticity of the repaired image is solved, andthe repaired image is more natural and more real.
Owner:SOUTH CHINA UNIV OF TECH

Scene segmentation method and device, computer equipment and storage medium

The invention relates to a scene segmentation method and device, computer equipment and a storage medium, and relates to the technical field of machine learning. The method includes: inputting the image to be identified into the deep neural network; carrying out depth separable convolution on the image through the down-sampling module; obtaining a first feature map of which the size is smaller than that of the image; performing hole convolution on the first feature map through a hole space pyramid pooling module; obtaining a second feature map of different scales, performing depth separable convolution on the second feature maps of different scales through an up-sampling module to obtain a third feature map with the same size as the image, and classifying each pixel in the third feature map through a classification module to obtain a scene segmentation result of the image. According to the invention, the calculation amount of scene segmentation through the deep neural network can be reduced, and the accuracy of scene segmentation through the deep neural network can be ensured.
Owner:BEIJING DAJIA INTERNET INFORMATION TECH CO LTD

Image super-resolution method based on generative adversarial network

The invention discloses an image super-resolution method based on a generative adversarial network. The method comprises the following steps: obtaining a training data set and a verification data set;constructing an image super-resolution model, wherein the image super-resolution model comprises a generation network model and a discrimination network model; initializing weights of the establishedgenerative network model and the discriminant network model, initializing the network model, selecting an optimizer, and setting network training parameters; simultaneously training the generative network model and the discriminant network model by using a loss function until the generative network and the discriminant network reach Nash equilibrium; obtaining a test data set and inputting the test data set into the trained generative network model to generate a super-resolution image; and calculating a peak signal-to-noise ratio between the generated super-resolution image and a real high-resolution image, calculating an evaluation index of the image reconstruction quality of the generated image, and evaluating the reconstruction quality of the image. According to the method, the performance of reconstructing the super-resolution image by the network is improved by optimizing the network structure, and the problem of image super-resolution is solved.
Owner:SOUTH CHINA UNIV OF TECH

Convolutional neutral network-based worker absence-from-post detection method

The invention discloses a convolutional neutral network-based worker absence-from-post detection method. According to different characteristics extracted from monitoring images, the images are classified via an image classification algorithm, and whether workers are absent from posts can be detected via a convolutional neutral network training model. Monitoring workers in a company can be helped to process monitoring data efficiently via the worker absence-from-post detection method, whether the workers are at the posts can be determined, and whether the workers are absent from the posts can be accurately detected in working environment; a conventional convolutional neutral network structure can be improved so as to be applied to indoor working scenarios such as companies, enterprises and the like; worker absence-from-post detection can be realized, and the worker absence-from-post detection method is well applied to solving the problem.
Owner:NANJING NARI GROUP CORP +1

Adversarial-based lightweight network semantic segmentation method

The invention relates to an adversarial-based lightweight network semantic segmentation method, which is used for solving the problems of low prediction accuracy, low network processing speed and difficulty in meeting the requirement of real-time prediction. The invention provides a lightweight semantic segmentation method based on adversarial from the perspective of improving semantic segmentation speed and precision. The method comprises the following steps: firstly, improving the network information acquisition capability by reducing the number of channels, reducing the parameter quantity in jump connection by utilizing asymmetric convolution, increasing the receptive field of a feature map by utilizing cavity convolution and disturbing the operation of the channels, and constructing alightweight asymmetric encoding and decoding semantic segmentation network; using confrontation ideas, and judging the segmented image and the calibrated semantic label by using a judgment network, designing a judgment loss function and a segmentation loss function, and alternately updating the segmentation network and the judgment network by using a back propagation method until the judgment network cannot distinguish the label and the real label generated by the segmentation network, thereby realizing semantic segmentation of the image. According to the method, the lightweight model and theadversarial idea are utilized, so that the segmentation precision is relatively high while the real-time performance of the segmentation network is ensured.
Owner:BEIJING UNIV OF TECH

An image super-resolution method based on a channel attention mechanism and multilayer feature fusion

The invention relates to an image super-resolution method based on a channel attention mechanism and multilayer feature fusion, and the method comprises the steps of directly extracting the original features of a low-resolution image at the beginning of a residual branch by using a single-layer convolutional layer based on deep learning; using six cascaded convolutional circulation units based ona channel attention mechanism and multi-layer feature fusion to extract accurate depth features; carrying out upsampling on the depth features through a deconvolution layer, and carrying out dimensionality reduction on the upsampled features through a single-layer convolution layer to obtain a residual error of the high-resolution image; carrying out up-sampling on the low-resolution image by using a bicubic interpolation method in a mapping branch to obtain mapping of the high-resolution image; and adding the mapping and the residual of the high-resolution image pixel by pixel to obtain a final high-resolution image. The method is reasonable in design, fully considers the difference between the feature channels, efficiently utilizes the hierarchical features, and maintains a higher operation speed while obtaining higher accuracy.
Owner:ACADEMY OF BROADCASTING SCI STATE ADMINISTATION OF PRESS PUBLICATION RADIO FILM & TELEVISION +1

Remote sensing image terrain classification method based on lightweight semantic segmentation network

ActiveCN111079649APreserve spatial featuresMulti-context featuresScene recognitionData setTest sample
The invention discloses a remote sensing image terrain classification method based on a lightweight semantic segmentation network, and mainly solves the problems of low remote sensing image terrain classification precision and low training speed caused by insufficient utilization of image space and channel feature information and a huge model in an existing method. According to the scheme, the method includes obtaining a training sample and a test sample in a remote sensing image terrain classification data set; constructing and introducing a lightweight remote sensing image terrain classification model capable of broadening channel decomposition hole convolution, and designing an overall loss function of a concerned terrain edge; inputting a training sample into the constructed terrain classification model for training to obtain a trained model; and inputting the test sample into the trained model, and predicting and outputting a terrain classification result in the remote sensing image. According to the method of the invention, the feature expression capability is improved, the network parameters are reduced, the average precision and the training speed of remote sensing image terrain classification are improved, and the method can be used for obtaining the terrain distribution condition of a remote sensing image.
Owner:XIDIAN UNIV

A meta-learning algorithm based on stepwise gradient correction of a meta-learner

The invention discloses a meta-learning algorithm based on stepwise gradient correction of a meta-learner, and the algorithm comprises the steps: firstly, obtaining training data with noise marks anda small amount of clean unbiased metadata sets; establishing a meta-learner, namely a teacher network, on the metadata set relative to a classifier, namely a student network established on the training data set; and carrying out united updating of student network parameters and teacher network parameters by using random gradient descent; obtaining a student network parameter gradient update function through a student network gradient descent format; feeding the network parameters back to the teacher network, and updating the teacher network parameters by using metadata to obtain a corrected student network parameter gradient format; and then updating the student network parameters by using the correction format. Accordingly, the student network parameters can achieve better learning in thecorrection direction, and the over-fitting problem of noise marks is weakened. The method has the characteristics of easiness in understanding, realization, interpretability and the like of a user, and can be robustly suitable for an actual data scene containing noise marks.
Owner:XI AN JIAOTONG UNIV

Method for labeling and complementing gastric cancer pathological slice based on pseudo-label iterative annotation

The invention discloses a method for labeling and complementing gastric cancer pathological slices based on pseudo-label iterative annotation. The method comprises the steps that 1), pseudo-label samples are produced by using the original positive samples and the original negative samples of the gastric cancer pathological slices; 2), image segmentation is conducted on the pseudo-label samples, and the pseudo-label samples are used as training images and transmitted to U-Net to be trained; 3), data augmentation is conducted on the original positive samples and transmitted to the trained U-Netin step 2) to be tested, reduction is conducted based on an augmentation manner, and finally weighted averaging is conducted on all images and the images are integrated to obtain a gastric diseased probability graph; 4), the parts of which the gastric cancer diseased probability is higher than a threshold value are screened out, extracted and spliced to the original negative samples to generate the pseudo-label samples of the next iteration; iteration is constantly conducted on the above processes to finally obtain the gastric cancer pathological slices which are completely annotated. By meansof the method, human resources needed to be consumed by slice annotation are greatly reduced, the quantity and quality of a training data set are improved, and probability is provided for training amore accurate deep learning model.
Owner:ZHEJIANG UNIV
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