110 results about "Graph encoding" patented technology

Techniques for encapsulating video streams containing multiple coded views in a media file are described herein. In one example, a method includes parsing a track of video data, wherein the track includes one or more views. The method further includes parsing information to determine whether the track includes only texture views, only depth views, or both texture and depth views. Another example method includes composing a track of video data, wherein the track includes one or more views and composing information that indicates whether the track includes only texture views, only depth views, or both texture and depth views.

Techniques for encapsulating video streams containing multiple coded views in a media file are described herein. In one example, a method includes parsing a track of multiview video data, wherein the track includes at least one depth view. The method further includes parsing information to determine a spatial resolution associated with the depth view, wherein decoding the spatial resolution does not require parsing of a sequence parameter set of the depth view. Another example method includes composing a track of multiview video data, wherein the track includes the one or more views. The example method further includes composing information to indicate a spatial resolution associated with the depth view, wherein decoding the spatial resolution does not require parsing of a sequence parameter set of the depth view.

Techniques for encapsulating video streams containing multiple coded views in a media file are described herein. In one example, a method includes parsing a track of video data, wherein the track includes one or more views. The method further includes parsing information to determine whether a texture view or a depth view of a reference view is required for decoding at least one of the one or more views in the track. Another example method includes composing a track of video data, wherein the track includes one or more views and composing information that indicates whether a texture view or a depth view of a reference view is required for decoding at least one of the one or more views in the track.

A method and device for coding screen content into a bitstream by selecting a color palette table for a coding unit (CU) of screen content, creating a color index map having indices for the coding unit (CU), and encoding the selected color palette table and the color index map for the CU into a bitstream.

Techniques for encapsulating video streams containing multiple coded views in a media file are described herein. In one example, a method includes parsing a track of multiview video data, wherein the track includes one or more views, including only one of a texture view of a particular view and a depth view of the particular view. The method further includes parsing a track reference to determine a dependency of the track to a referenced track indicated in the track reference. Track reference types include ‘deps’ that indicates that the track includes the depth view of the particular view and the reference track includes the texture, ‘tref’ that indicates that the track depends on the texture view which is stored in the referenced track, and ‘dref’ that indicates that the track depends on the depth view which is stored in the referenced track.

The invention discloses an unsupervised target detection method and a system based on a variational auto-encoder and a Gaussian mixture model, and the method comprises the steps: converting an input image into a feature map of H * W dimensions, namely H * W cells, through a backbone network, coding the feature map into a hidden variable of which the prior distribution accords with the Gaussian mixture model, and carrying out the image reconstruction through a decoder according to the hidden variable, and comparing the reconstructed image with the input image, and calculating a loss function, so that a neural network is trained, an encoder obtains information such as the category and the position of an object in the image, and unsupervised target detection is realized. According to the method, the space attention mechanism and the Gaussian mixture model are combined, end-to-end target detection and classification can be achieved, meanwhile, good performance is still achieved under the condition that a large number of objects exist, and good expansibility is achieved.

The invention relates to a social media rumor detection method and system based on a hierarchical heterogeneous graph neural network, and the system comprises: a data preprocessing module which is used for preprocessing text data, extracting user static features, and constructing a hierarchical heterogeneous graph structure; an event encoding module which is used for encoding a text containing propagation and diffusion structure information; a user coding module which is used for learning user behavior features; a global heterogeneous graph coding module which is used for capturing rich global structure information between the event and the user; and a rumor detection label output module which is used for fusing the text information, the user behavior features and the global heterogeneous graph information to complete label prediction work of rumor detection. According to the method, local inline relationships between users and between texts can be effectively learned, user and text representations containing adjacent node information are generated, a global structure relationship between the user and an event is modeled and learned, and finally the authenticity of the event is identified.

The invention provides cross-language unsupervised classification with multi-view transfer learning. Embodiments of an unsupervised cross-language sentiment classification model, which may be referred to as a multi-view encoder classifier (MVEC), utilizing an unsupervised machine translation (UMT) system and a language discriminator are presented. Unlike previous Language Model (LM)-based fine tuning methods that adjust parameters based only on classification errors of training data, embodiments employ an encoder-decoder framework of UMT as a regularization component on shared network parameters. In one or more embodiments, a cross-language encoder of an embodiment learns a shared representation, which is effective for reconstructing input sentences of both languages and generating more representative views from the input for classification. Experiments on five language pairs prove that the embodiment of the MVEC is obviously superior to other models for 8/11 sentiment classification tasks.

A computer-implemented method for securing software installation through deep graph learning includes extracting a new software installation graph (SIG) corresponding to a new software installation based on installation data associated with the new software installation, using at least two node embedding models to generate a first vector representation by embedding the nodes of the new SIG and inferring any embeddings for out-of-vocabulary (OOV) words corresponding to unseen pathnames, utilizing a deep graph autoencoder to reconstruct nodes of the new SIG from latent vector representations encoded by the graph LSTM, wherein reconstruction losses resulting from a difference of a second vector representation generated by the deep graph autoencoder and the first vector representation represent anomaly scores for each node, and performing anomaly detection by comparing an overall anomaly score of the anomaly scores to a threshold of normal software installation.

An Information supply method for displaying a message received or generated by a first electronic device on a display of a second electronic device. The information supply method includes the steps of: receiving a request of displaying the message; generating a word string according to the request, wherein the word string includes at least one word pattern and at least one graph encoding correspondingly representing the at least one word pattern; transmitting the word pattern and the graph encoding from the first electronic device to the second electronic device; and receiving the at least one word pattern and the at least one graph encoding, and displaying the at least one word pattern corresponding to the message received or generated by the first electronic device on the display according to the graph encoding.

The invention discloses a pavement disease detection method based on deep learning. A large number of normal road pictures are input, road disease image samples are not needed, a system result contains two neural networks, the structure of the first neural network is 'encoder-decoder-encoder', the first neural network is mainly responsible for encoding and decoding pictures, the encoding process is to convert the pictures into a group of vector data through the neural network, and finally, the decoded vector is compared with the decoded vector of the original picture. According to the pavementdisease detection method based on deep learning, which relates to the technical field of traffic pavement detection, a road disease sample does not need to be used for analysis, a large amount of manpower and material resources are saved, a plurality of neural networks are used, a large number of normal road pictures are analyzed for coding, decoding and re-coding training, the training quality is judged through a discrimination network, and finally, the difference between coded pictures and original picture codes is analyzed to detect whether a road has diseases or not.

The embodiment of the invention discloses an event extraction method, a related device, equipment and a storage medium, which are used for converting a sentence-level natural language into nodes and edges and then converting the nodes and the edges into semantic features to perform event extraction, so that the accuracy of event acquisition can be ensured. The method of the embodiment comprises: obtaining a to-be-processed text; generating abstract semantic representation according to the to-be-processed text, wherein the abstract semantic representation comprises nodes in one-to-one correspondence with the words and edges used for connecting the nodes; carrying out semantic coding processing on the abstract semantics and the text representation to obtain a semantic embedding vector, wherein the semantic embedding vector is used for representing semantic features between each word and the event; carrying out graph coding processing on the abstract semantic representation to obtain a graph embedding vector, wherein the graph embedding vector is used for representing structural features of nodes connected through edges; splicing the semantic embedding vector and the graph embedding vector to obtain a spliced feature vector; and identifying the spliced feature vector, and outputting a target event.

The invention belongs to the field of computer artificial intelligence of natural language generation, and discloses a multi-round dialogue reply generation system and method based on a relational graph attention network. Comprising the steps of obtaining multi-round dialogue input content for preprocessing, obtaining semantic information representation of each round of utterance, and encoding statement semantic information of each round of utterance to obtain semantic representation of a dialogue context; then, a graph attention network is adopted to capture autocorrelation in multiple rounds of dialogues and related features among dialogues, and relation position codes are introduced into the graph attention network to illustrate sequence information containing utterances, so that high-level semantic representation of a graph coding layer is obtained; and finally, taking the context semantic information representation of the dialogue and the advanced semantic representation of the attention coding of the relational graph as input, and decoding by using a GRU model to generate a final dialogue reply output representation. According to the method, the generation quality of the multi-round dialogue reply is remarkably improved, and the generated reply is more coherent and meaningful.

The invention discloses a textile material CT image segmentation method and device based on a convolutional neural network. The method comprises the steps: firstly building a segmentation model comprising an encoder and a decoder; secondly, acquiring a CT image of the textile material to be segmented; inputting the data into a trained segmentation model; performing feature extraction on the inputimage to obtain an encoding feature map via the encoder; inputting the encoding feature maps of the multiple levels into the corresponding levels of the decoder for feature fusion to obtain a first fusion feature map via the encoder; decoding the first fusion feature map to obtain a decoded feature map via the decoder; fusing the decoding feature maps of the middle level and the deep level to obtain a second fused feature map. The textile material CT image segmentation method solves the technical problems that in an existing textile material CT image segmentation method, manual segmentation isadopted, the segmentation difficulty is large, the segmentation process is tedious, time and labor are wasted, the segmentation result depends on experience and knowledge of operators to a large extent, and the segmentation result is difficult to reappear.

The embodiment of the invention provides an method and device for training an interaction prediction model and a method and device for using the interaction prediction model. The training method comprises the steps: firstly, constructing a dynamic interaction graph based on an interaction event sequence,determining a first node and a second node corresponding to a current interaction event from the dynamic interaction graph, wherein the first node points to two historical nodes corresponding to a previous interaction event in which the first node participates; using a graph coding network to obtain coding vectors corresponding to the two historical nodes; and inputting the coding vectors and the respective occurrence moments of the two interaction events into a prediction representation network to obtain a first representation vector of the first node; inputting the attribute characteristics of the second node into an attribute coding network to obtain a second attribute vector; predicting the interaction probability according to the first representation vector and the second attribute vector, determining the loss according to the interaction probability, and updating the graph coding network, the prediction representation network and the attribute coding network.

The invention discloses a sparse graph coding-based hyperspectral remote sensing image eigen decomposition method and system, and relates to the field of image processing. According to the method, the problem that the precision of generating the reflectivity component of the hyperspectral image is low due to the fact that the surface feature boundary cannot be effectively kept when an existing intrinsic decomposition method is applied to the hyperspectral image is solved. The method comprises the following steps: acquiring a hyperspectral remote sensing image; averaging a hyperspectral remote sensing image geometrically in a spectral dimension to remove spectral change caused by geometric distribution on the surface of an object, so that the image is geometrically averaged in a spatial dimension; eliminating spectral change caused by illumination changing along with spatial distribution to obtain an image, and obtaining a sparse graph coding dictionary of each pixel in the hyperspectral remote sensing image; obtaining a similarity matrix of a sparse graph according to the sparse graph coding dictionary of the hyperspectral remote sensing image; obtaining a reflectivity component of the hyperspectral image according to the sparse image similarity matrix; the system comprises an acquisition module, a construction module, a calculation module and a decomposition module.

The invention discloses an RGB image guided depth map super-resolution method based on a joint implicit image function, and the method comprises the steps: building a deep neural network model which is used for depth map super-resolution image restoration and comprises an RGB image encoder, a depth map encoder and a JIIF decoder; adopting an implicit neural representation method to establish a joint implicit image function suitable for an RGB-guided depth map super-resolution task, and carrying out modeling on multi-modal input; respectively extracting features from the RGB image and the input low-resolution depth map through two encoders of the established deep neural network model; predicting and outputting the depth value of each pixel under the high resolution through a JIIF decoder; and realizing the super-resolution image recovery of the depth map guided by the RGB image based on the combined implicit image function. According to the method, the image recovery effect superior to that in the prior art is achieved on the RGB image guided depth image super-resolution recovery task, and the method has better interpretability.

The invention provides a multi-dimensional enhanced epileptic seizure prediction system based on a graph convolutional network, the scheme takes a multi-channel spatial relationship as a breakthrough opening, the contribution degree of the multi-channel spatial relationship to epileptic seizure prediction is explored from three dimensions of frequency + space + time. The model comprises three parts, respectively an information reconstruction space, a graph encoder and a space-time predictor. The information reconstruction space and the graph encoder mentioned by the model allow feature enhancement and feature extraction of richer epilepsy electroencephalogram signals, and especially explores the correlation among electroencephalogram channels, so that feature representation is enhanced, and the epilepsy electroencephalogram seizure prediction accuracy is improved; meanwhile, the core structure of the space-time predictor in the scheme adopts a gating circulation unit which is used for exploring the law of epilepsy electroencephalogram signals on the time level, and the purpose of improving the model operation efficiency is achieved by reducing the network parameter scale.