561 results about "Task learning" patented technology

The invention relates to a scene and target identification method and device based on multi-task learning. The method comprises the steps that pictures containing different scenes and targets are collected as image sample data; the image sample data is subjected to manual label marking, and target class labels and scene class labels are obtained; a multi-layer convolutional neural network model is built, and network initialization is conducted; the image sample data and the corresponding target class labels are adopted for pre-training the built model till convergence, and a target identification model is obtained; based on a multi-task learning technology, network branches are added into a specific layer of the target identification model, random initialization is conducted, and a multi-task network is obtained; the image sample data and the corresponding scene class labels and target class labels are adopted for e-training the multi-task network till convergence, and a multi-task learning model is obtained; new image data is input to the multi-task learning model, and classification results of scene and target identification of images are obtained. Accordingly, the single task identification precision is improved.

The invention provides a face attribute recognition method of a deep neural network based on cascaded multi-task learning and relates to the computer vision technology. Firstly, a cascaded deep convolutional neural network is designed, then multi-task learning is used for each cascaded sub-network in the cascaded deep convolutional neural network, four tasks of face classification, border regression, face key point detection and face attribute analysis are learned simultaneously, then a dynamic loss weighting mechanism is used in the deep convolutional neural network based on the cascaded multi-task learning to calculate the loss weights of face attributes, finally a face attribute recognition result of a last cascaded sub-network is used as the final face attribute recognition result based on a trained network model. A cascading method is used to jointly train three different sub-networks, end-to-end training is achieved, the result of face attribute recognition is optimized, different from the use of fixed loss weights in a loss function, a difference between the face attributes present is considered in the invention.

A multi-task learning system and method for predicting travel demand on an associated transportation network are provided. Observations corresponding to the associated transportation network are collected and a set of time series corresponding to travel demand are generated. Clusters of time series are then formed and for each cluster, multi-task learning is applied to generate a prediction model. Travel demand on a selected segment of the associated transportation network corresponding to at least one of the set of time series is then predicted in accordance with the generated prediction model.

The invention provides a scene understanding method based on multi-task learning. The method includes: multi-task learning having unstable homoscedasticity, multi-task likelihood function, and scene understanding model. The method includes the following steps: first executing weighted linear sum on the loss of each individual task, learning the optimal task weight, inducting a multi-task loss function, defining a probability model, defining the probability as the gaussian function of the average values that are output by the model, eventually modeling pixel-level learning regression and classified output, comprising semantic division, incidence division and depth regression. According to the invention, the scene understanding model can learn multi-task weight, is more advantageous than models that independently train each task in that the method herein reduces computing amount, increases learning efficiency and prediction precision and can be real-timely operated.

The invention provides an event extraction method and system fusing dependency information and a pre-trained language model. The method comprises the steps of taking a dependency syntax tree of a sentence as input, learning dependency syntax features by using a graph convolutional neural network, adding a dependency relationship prediction task, capturing a more important dependency relationship in a multi-task learning mode, and finally enhancing underlying syntax expression by using a BERT pre-training language model to complete event extraction of Chinese sentences. Therefore, the performance of trigger word extraction and argument extraction under the event extraction task is improved.

The invention provides a text classification method based on deep multi-task leaning. The method comprises the steps that by means of a recurrent neural network obtained through other task training and by combining the learning ability of a convolutional neural network, additional document representation is obtained, that is to say, a large amount of external information is introduced, semantic representation of a document is extended, and the problem that training data is insufficient is effectively solved. Accordingly, compared with a traditional multi-task leaning method, the convolutional neural network is used for conducting feature extraction on bottom-layer features of an auxiliary task, the features of other tasks can be utilized for being effectively transferred to the current task, and the performance of text classification is improved.

The invention relates to the technical field of road traffic image recognition, and discloses a lane line multi-task learning detection method based on road segmentation, and the method comprises thesteps: constructing a multi-task learning network, carrying out the recognition and processing of an inputted road image, and outputting road segmentation data and lane line detection data; constructing a link coding structure, and connecting the two sub-networks; and performing alternate fine adjustment training on the two sub-networks, correcting and improving the lane line detection data precision by adopting a cross entropy loss function, and finally outputting corrected lane line detection data. The detection scale is diversified, the robustness is improved, the detection precision in a complex scene is improved, and the speed is high. The two sub-networks are connected through the link coding structure, and the information amount obtained by the feature map is increased through parameter hard sharing of the two sub-networks.

A method for determining an emotional state of a subject taking an assessment. The method includes eliciting predicted facial expressions from a subject administered questions each intended to elicit a certain facial expression that conveys a baseline characteristic of the subject; receiving a video sequence capturing the subject answering the questions; determining an observable physical behavior experienced by the subject across a series of frames corresponding to the sample question; associating the observed behavior with the emotional state that corresponds with the facial expression; and training a classifier using the associations. The method includes receiving a second video sequence capturing the subject during an assessment and applying features extracted from the second image data to the classifier for determining the emotional state of the subject in response to an assessment item administered during the assessment.

The invention discloses a user dynamic preference oriented commodity sequence personalized recommendation method. The method comprises the steps of extracting a commodity sequence under the same usersimilarity score to construct a commodity score vector; extracting a user sequence of similar scores of the same commodity to obtain a user score vector; combining user personal information, commoditybasic attribute information, user and article comments and commodity pictures; achieving feature extraction of a user and a commodity based on multi-task learning, taking a user feature vector and afeature vector of a historical commodity sequence of the user feature vector as input, achieving generation of the commodity sequence by training a coder-decoder, and accurately learning recommendation of the optimal commodity sequence in combination with a search strategy. According to the invention, based on the multi-modal user-commodity data, the user features and the commodity features are highly extracted and fused, personalized recommendation of the commodity sequence for user preferences is realized, and the user experience is improved.

The invention discloses a company announcement processing method for multi-task learning and a server, and the method comprises the steps: inputting historical announcement data into a sharing layer of a multi-task learning model, and carrying out the pre-training of the historical announcement data through Bert; inputting the data set corresponding to the processing task into a task layer of themulti-task learning model to train the multi-task learning model; obtaining current announcement data, and inputting the current announcement data into the trained multi-task learning model to obtaina task processing result. The multi-task learning model is constructed through using in a transfer learning and multi-task learning mode, and the method has the advantages of being high in learning efficiency, high in generalization, low in manual maintenance cost, high in accuracy of multiple tasks, high in recall rate and convenient for engineering deployment and maintenance.

The invention relates to a multi-modal fusion emotion recognition system and method based on multi-task learning and an attention mechanism and an experimental evaluation method, and aims to solve the problems that in the prior art, a multi-modal emotion recognition process without introducing a multi-modal fusion mechanism is low in efficiency and accuracy. The invention belongs to the field of human-computer interaction, and provides a multi-modal fusion emotion recognition method based on combination of multi-task learning and an attention mechanism, and compared with single-modal emotion recognition work, the multi-modal fusion emotion recognition method based on combination of multi-task learning and the attention mechanism is wider in application. Multi-task learning is utilized to introduce an auxiliary task, so that the emotion representation of each mode can be more efficiently learned, and an interactive attention mechanism can enable the emotion representations among the modes to mutually learn and complement each other, so that the recognition accuracy of the multi-mode emotion is improved; experiments are carried out on the multi-modal data sets CMU-MOSI and CMU-MOSEI, the accuracy and the F1 value are both improved, and meanwhile the accuracy and efficiency of emotion information recognition are improved.

The invention belongs to the technical field of remote sensing image ground object change detection, and particularly relates to a remote sensing image ground object change detection method based on multi-task learning, which is used for solving the problem of error accumulation caused by the fact that the ground object change detection precision depends on the ground object classification precision in the prior art. A multi-task learning ground object change detection model is adopted and comprises two semantic segmentation model branches and a change detection model branch. A semantic segmentation model is constructed through a segmentation network, a feature extraction module of the model can effectively extract features of a remote sensing image, then a twin network is constructed to train a ground object change detection model, and a multi-task learning mechanism is constructed. In conclusion, the ground object change detection area can be determined, change detection results of different ground objects and ground object types before and after area change can be obtained, meanwhile, the problem of error accumulation is avoided, and the change detection precision is improved.

The embodiment of the invention discloses an acoustic model training method and device, computer equipment, and a storage medium; the method comprises the following steps: obtaining supervised voice data and non-supervision voice data, wherein the supervised voice data refers to voice data with artificial marks, and the non-supervision data refers to the voice data with machinery marks; extracting voice features from the supervised voice data and non-supervision voice data; using a depth learning network structure to respectively carry out multiple-task learning including a supervised learning task and a non-supervision learning task for the voice features of the supervised voice data and non-supervision voice data, and training to obtain an acoustic model. The half-supervision acoustic model training based on multiple-task learning can save the artificial marking voice data cost needed for training the acoustic model, and no expensive artificial marked voice data is needed, thus continuously improving voice recognition performance.

The invention discloses a video recommendation method based on multi-modal video content and multi-task learning. The method comprises the following steps: extracting visual, audio and text features of a short video through a pre-trained model; fusing the multi-modal features of the video by adopting an attention mechanism method; learning feature representation of the social relationship of the user by adopting a deep walk method; proposing a deep neural network model based on an attention mechanism to learn multi-domain feature representation; embedding the features generated based on the above steps into a sharing layer as a multi-task model, and generating prediction results through a multi-layer perceptron. According to the method, the attention mechanism is combined with the user features to fuse the video multi-modal features, so that the whole recommendation is richer and more personalized; meanwhile, because of multi-domain features and with consideration of the importance ofinteraction features in recommendation learning, a deep neural network model based on an attention mechanism is provided, so that learning of high-order features is enriched, and more accurate personalized video recommendation is provided for users.

The invention discloses a microscopic image cell identification method based on multi-task learning. The method comprises the following steps: firstly, labeling an acquired microscopic image by adopting a multi-labeling mode; then segmenting the microscopic image containing the multiple labels to form a training set; training a pre-constructed cell identification model by utilizing the training set containing the multiple labels; and finally, carrying out cell identification on the microscopic image to be identified by utilizing the trained cell identification model to obtain the category andthe characteristic attribute of each cell in the microscopic image to be identified. Compared with the prior art, the method provided by the invention adopts a multi-labeling mode to label the microscopic image, so that the model obtained by training can realize multi-detection, and the multi-detection can ensure the accuracy of a final detection result.

The invention discloses an image classification method and system based on hierarchical multi-task learning. The method comprises the following steps of: firstly, carrying out hierarchical category labeling on images in a training sample set from coarse to fine by utilizing a relation between image categories through experience knowledge of experts in some professional fields; secondly, constructing a hierarchical deep convolutional neural network comprising a feature extraction module, a coarse classification layer and a fine classification layer; secondly, training the coarse classificationlayer and the fine classification layer by adopting a multi-task learning algorithm; and finally, classifying the input test images by using the trained hierarchical deep convolutional neural network.The hierarchical structure information between the image categories is combined with the convolutional neural network, the hierarchical deep convolutional neural network is designed, and a multi-tasklearning algorithm is utilized to realize coarse-to-fine classification of the images.

The invention provides a multi-task learning based method for recognizing race and gender through a human face image, relates to the technical fields such as the digital image processing field, the mode recognizing field, the computer vision field and the physiology field, and aims to solve the problems of recognizing of the race and the gender from a static human face or a video human face under a plurality of occasions. The multi-task learning method is a learning method for improving the learning performance through related task learning and has the advantages of recognizing the learning difference between the tasks, sharing related features of the tasks, improving the learning performance through the relevance, and reducing the high-dimension small sample over-learning problem. The multi-task learning method can be applied to the human face based race and gender recognizing; different semantics are treated as different tasks, and on that basis, the semantics-based multi-task feature selection is proposed. With the adoption of the method for recognizing the race and the gender, the generalization capacity of a learning system and the recognizing effect can be obviously improved.

The invention discloses a driver fatigue state rapid detection method based on deep learning, and the method comprises the following steps: (1), collecting a color image of a driver during driving, detecting a face part of the driver in the image through a deep learning method, and marking the face part through a regression frame; (2) taking the face boundary regression frame as input, inputting the face boundary regression frame into a multi-task learning network, and finally detecting to obtain a face key point of a face and an attitude angle of a head; and (3) establishing a space-time fatigue feature sequence by using the face key points and the head attitude angle, inputting the feature sequence into a fatigue recognition deep learning network as input, and finally outputting a fatigue state recognition result. Real-time performance and accuracy requirements of driver fatigue state detection are fully considered, a corresponding optimization method is designed by optimizing compression and fatigue characteristics of a deep learning network model on the premise of ensuring accuracy, the volume of the network is compressed to the maximum extent, and the operation speed of the algorithm is increased.

The invention provides a robot sequence task learning method based on visual simulation. The robot sequence task learning method based on the visual simulation is used for guiding a robot to simulateand execute human actions from a video containing the human actions. The robot sequence task learning method comprises the following steps of (1) identifying object types and masks by using a region-based mask convolutional neural network according to an input image; (2) calculating actual plane physical coordinates (x, y) of objects according to the masks; (3) identifying atomic actions in a target video; (4) converting an atomic action sequence and the identified object types into a one-dimensional vector; (5) inputting the one-dimensional vector into a task planner, and outputting a task description vector capable of guiding the robot; and (6) controlling the robot to simulate a sequence task in the target video by combining the task description vector and the object coordinates. According to the robot sequence task learning method based on the visual simulation, the video and the image serve as input, the objects are recognized, a task sequence is deduced, the robot is guided to simulate the target video, and meanwhile, the generalization performance is high, so that the simulation of the task can still be completed under different environments or object types.

The invention discloses an Alzheimer's disease classification and prediction system based on multi-task learning, and relates to an Alzheimer's disease classification and prediction system. The objective of the invention is to solve the problem that an existing Alzheimer's disease classification system cannot judge whether a mild cognitive impairment individual will be transformed into Alzheimer'sdisease or not. The system comprises a an image processing main module, a clinical index processing main module, a neural network main module, a training main module and a detection main module; wherein the image processing main module is used for acquiring a head image, preprocessing the acquired head image to obtain a preprocessed image, and inputting the preprocessed image into the training main module and the detection main module; wherein the clinical index processing main module is used for selecting clinical indexes, acquiring feature vectors of the clinical indexes and inputting the feature vectors of the clinical indexes into the training main module and the detection main module; and the neural network main module is used for building an Alzheimer's disease classification and prediction model. The system is applied to the technical field of intelligent medical detection.