224 results about "Residual Blocks" patented technology

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention provides a remote sensing image super-resolution reconstruction method and system based on residual hierarchical Transform, and relates to the technical field of digital image processing, and the method comprises the following steps: obtaining a low-resolution image; performing shallow feature extraction on the input low-resolution image through convolutional layer operation to obtain a shallow feature image; secondly, on the basis of the design concept that residual blocks are nested in residual groups, a multi-scale feature calibration mechanism, a Transform space-channel attention mechanism based on a hierarchical window and a space-gate feed-forward network are combined, and a depth feature image is obtained; and finally, performing fusion and up-sampling on the shallow feature image and the depth feature image through convolution and sub-pixel convolution operation to obtain a final remote sensing image super-resolution reconstruction result. By means of the scheme, the remote dependency relationship in the remote sensing image can be effectively modeled, the feature extraction capacity can be improved, and the problem that large-scale window self-attention calculation is high in complexity in a Transform mechanism can be solved.

The invention discloses a method for predicting the elastic property of a fiber reinforced composite material, and aims to solve the problems of low precision, low efficiency and difficulty in processing multi-scale correlation of a traditional method. The method comprises the following steps: firstly, generating a representative volume unit by using a random algorithm, and constructing a high-quality training data set by combining Sobol sequence sampling and an SMOGN data enhancement technology; secondly, designing and training a deep neural network embedded with residual blocks and physical constraints, and improving the generalization ability of the model through Bayesian optimization adaptive parameter adjustment; and finally, establishing a plurality of macrostructure models, and realizing end-to-end prediction of the multi-scale elastic performance. The method realizes breakthrough in prediction precision and calculation efficiency: the time consumed by single analysis is shortened from several hours to a minute-second level, and the average prediction error is lower than 5%. The technology can be widely applied to the fields of aerospace, new energy automobiles, wind power and the like, and intelligent support is provided for design and manufacturing of high-performance composite materials.

The invention provides a self-distillation model compression method and device, an electronic product and a medium. The method comprises the steps that a teacher model needing model compression is constructed, the basic structure of the teacher model is divided into a plurality of residual blocks, and student models are constructed according to the residual blocks; through knowledge distillation of each model, enhancing result knowledge; enhancing process knowledge through attention map mapping of feature maps of the teacher model and each student model; according to the enhancement result of the effective knowledge and the enhancement result of the process knowledge, calculating a loss function of the teacher model and each student model; according to the loss function obtained through calculation, a student model with the classification effect closest to the teacher model is extracted to serve as a compression model of the teacher model. According to the self-distillation model compression method and device, the electronic product and the medium provided by the invention, a method technical support is provided for forming a simple, convenient, high-automation and user-friendly model compression and acceleration tool.

The invention relates to the technical field of image super-resolution, and provides an image super-resolution reconstruction method, and the method comprises the steps: obtaining a to-be-reconstructed low-resolution image, inputting the to-be-reconstructed low-resolution image into a trained super-resolution image reconstruction model, and obtaining a super-resolution reconstruction image corresponding to the to-be-reconstructed low-resolution image. A shallow layer feature extraction module in the super-resolution image reconstruction model is composed of a depth separable convolution sub-module and a standard convolution sub-module, and a deep layer feature extraction module is composed of a plurality of dense parallel residual blocks constructed based on an adaptive attention mechanism and a standard convolution layer which are connected in sequence. The reconstruction module is composed of a dynamic convolution layer, a moving window attention layer, a pixel reconstruction layer and a standard convolution layer, and solves the problems that an existing shallow feature extraction module cannot fully extract shallow features, the local modeling capability of a residual Swin Transform block in a deep feature extraction module is insufficient, and the reconstruction module cannot obtain features of a target area. Therefore, a high-quality super-resolution reconstructed image is obtained.

The invention relates to an EDSR (Enhanced Depth Super-Resolution) image super-resolution reconstruction method based on particle swarm optimization, and the method comprises the following steps of: (1) carrying out super-resolution reconstruction on an EDSR (Enhanced Depth Super-Resolution) image; the method comprises the following steps: firstly, inputting a low-resolution image data set as a training sample, defining an optimization space containing the number and stage of residual blocks, convolutional layer parameters, an attention module, an up-sampling mode and the like, initializing particle swarm optimization (PSO) parameters, and dynamically constructing a candidate network by particle position coding; a candidate network is dynamically constructed through particle position coding, and a residual block layer, a convolution layer, an attention module and an up-sampling module are sequentially configured. The candidate network is subjected to limited training, and individual and global optimal positions are updated through fitness function evaluation fusing PSNR and model complexity. And finally, a global optimal structure is selected for complete training for low-resolution image reconstruction, the detail reduction capability and the reasoning efficiency are remarkably improved, and a high-quality image is generated.

Techniques are described for decoding video data. A video decoder may determine chroma blocks in a chroma quantization group (QG) of the video data, determine a quantization parameter predictor that is the same for each of the chroma blocks of the chroma QG, determine an offset value that is the same for two or more of the chroma blocks of the chroma QG, determine a quantization parameter value for each of the two or more of the chroma blocks in the chroma QG based on the quantization parameter predictor and the offset value inverse quantize coefficients of one or more residual blocks for the chroma blocks based on the determined quantization parameter value, generate the one or more residual blocks based on the inverse quantized coefficients, and reconstruct the chroma blocks based on the one or more residual blocks.

Methods and devices for decoding video data are described. An example method includes in-loop filtering a current block of the video data using a neural network-based in-loop filter to generate an in-loop filtered current block, wherein the neural network-based in-loop filter is trained using an architecture comprising a U-Net architecture comprising one or more residual blocks and one or more transform blocks; and outputting the in-loop filtered current block.

The present invention discloses a method for intelligently identifying individual communication radiation sources in cross-period scenarios, relating to the technical field of radiation source individual identification. To address the problem of the degradation of individual distinguishability of radiation sources over time due to the inherent characteristics of communication signal radiation sources and the influence of complex electromagnetic environmental factors, the present invention, based on a generative adversarial network, constructs a generator using residual blocks to establish a mapping relationship between the feature distributions of data from two periods. By adding noise to the discriminator and discarding neurons with a certain probability, the model training process is stabilized and the risk of overfitting is reduced. Through adversarial training, a robust classifier with strong generalization capabilities is constructed. Through the above technical solutions, the present invention can achieve stable and effective individual identification of communication radiation sources in cross-period scenarios.

Methods and devices for decoding video data are described. An example method includes in-loop filtering a current block of the video data using a neural network-based in-loop filter to generate an in-loop filtered current block, wherein the neural network-based in-loop filter is trained using an architecture comprising a U-Net architecture comprising one or more residual blocks and one or more transform blocks; and outputting the in-loop filtered current block.

Disclosed is video encoding / decoding method and apparatus using a correlation of YCbCr. According to an aspect of the present disclosure, the video decoding method for predictively decoding a target block includes, receiving a bitstream and generating a residual block for a chroma block, generating reconstructed information in a luma block corresponding to the chroma block and reconstructed neighboring information about the luma block, generating reconstructed neighboring information about the chroma block, determining a scaling value and an offset value based on the reconstructed neighboring information about the chroma block and the reconstructed neighboring information about the luma block, generating a prediction block for the chroma block by applying the determined scaling value and offset value to the reconstructed information in the luma block, and generating a reconstructed block for the chroma block based on the residual block for the chroma block and the prediction block for the chroma block.

The invention relates to a point cloud visual fusion method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a semantic point cloud map, and obtaining a current picture; the current picture corresponds to the current pose, semantic segmentation is carried out on the current picture to obtain the category of pixels in the current picture, likelihood fields and likelihood field gradients of different categories are calculated, a residual block is constructed based on the semantic point cloud map, the likelihood fields and the likelihood field gradients, and the current pose is updated through the residual block. The updated current pose is used for updating the semantic point cloud map. According to the scheme provided by the invention, data fusion of the point cloud and the image can be realized without cross-sensor feature extraction and feature matching, and the accuracy of information in the semantic point cloud map is improved, so that the stability and precision of SLAM are improved.

Provided in the embodiments of the present application are a filtering method and apparatus, a coding method and apparatus, a decoding method and apparatus, and an electronic device. The filtering method based on a neural network comprises: generating an input parameter for a neural network filter on the basis of an image to be subjected to filtering; inputting the input parameter into the neural network filter, wherein the neural network filter includes a residual unit used for extracting image feature information, the residual unit includes a plurality of residual blocks which are sequentially connected, each residual block includes a plurality of convolutional layers which are arranged in parallel and have convolution kernels of different sizes, and the residual unit comprises different first set convolutional layers and second set convolutional layers, which are alternately arranged; and acquiring a filtered image which is output by the neural network filter with regard to the image to be subjected to filtering. The technical solution in the embodiments of the present application can reduce the computational complexity of a neural network filter and improve the filtering effect, thereby facilitating an improvement in video coding and decoding efficiency.

The invention discloses a confrontation and defense method based on dynamic gating mixed batch normalization. The method comprises the following steps: step 1, data acquisition and model initialization; 2, calling an adversarial attack algorithm to generate an adversarial sample; combining the adversarial sample and the clean sample to form a mixed sample; step 3-1, establishing a wide residual network model; the initial convolution layer passes through three network frameworks with the same structure, and the output of the last network framework sequentially passes through a standard batch normalization layer, a ReLU activation function, a global pooling layer and a full connection layer; each network framework comprises four residual blocks; 3-2, learning mixed batch normalization layer statistical distribution by using clean samples and adversarial samples; and 3-3, freezing parameters of the mixed batch normalization layer, and learning parameters of other network layers in the wide residual network model. According to the invention, by introducing a dual-path dynamic normalization and adaptive gating fusion mechanism, the robustness, generalization ability and calculation efficiency of the model are significantly improved.

A method for video processing applied to a decoder includes (i) receiving an input image; (ii) processing the input image by one or more convolution layers; (iii) processing the input image by multiple residual blocks by using partition information of the input image as reference so as to obtain reference information features; (iv) generating different-scales features based on the reference information features; (v) processing the different-scales features by multiple convolutional layer sets; (vi) processing the different-scales features by reference spatial attention blocks (RSABs) so as to form a combined feature; and (vii) concatenating the combined feature with the reference information features so as to form an output image.

A method and apparatus for joint sign prediction of transform coefficients of residual blocks in a video coding system are disclosed. At the encoder side, a transform coefficient region or an index value range is determined according to coding context associated with the current block. A set of signs associated with a set of selected transform coefficients are determined for joint sign prediction are determined according to the transform coefficient region or the index value range. Joint sign prediction for the set of signs is determined by selecting a hypothesis from a group of hypotheses for the set of signs that achieves a minimum cost. The sign prediction is then used for coding the set of signs. A corresponding method and apparatus for the decoder side is also disclosed.

The invention discloses an Alzheimer disease prediction method and system based on deep learning and electrocardiosignals. Firstly, electrocardiosignals are collected and preprocessed; then, constructing a deep learning prediction model which comprises a feature extraction module and a classification prediction module; the feature extraction module comprises a plurality of residual blocks, and each residual block comprises a convolution layer, a batch normalization layer, a nonlinear activation layer and a jump connection; the classification prediction module realizes a complete mapping process from a feature space to probability prediction through a multi-layer perceptron structure and a softmax function, and obtains a prediction probability of each electrocardiosignal fragment belonging to each category; and fusing the prediction results of the plurality of electrocardiosignal segments of the same sample by adopting a soft voting mechanism to obtain a final prediction result. The system comprises a signal acquisition module, a signal preprocessing module, a deep learning prediction module and a result output module. According to the method, Alzheimer's disease prediction is realized based on the electrocardiosignals, and the method is simple and convenient in acquisition mode, non-invasive, low in cost and suitable for large-scale screening.

In some example embodiments, there may be provided a method that includes receiving, at a machine learning model, an input for a task of the machine learning model, wherein the machine learning model comprises a plurality of residual blocks augmented with a plurality of augmented weight blocks that sample intermediate features from the plurality of residual blocks; applying the input to the machine learning model to perform the task, wherein the applying comprises applying the plurality of intermediate features, which are obtained from the plurality of residual blocks, to the plurality of augmented weight blocks to form a plurality of intermediate outputs; and generating an output of the machine learning model, wherein the output is generated using at least on a combination of the plurality of intermediate outputs. Related systems, methods, and articles of manufacture are also disclosed.

The invention provides a flight delay prediction method and system, computer equipment and a storage medium, and belongs to the technical field of traffic delay prediction.The method comprises the steps that firstly, a bacterial foraging algorithm (BFA) is optimized through a genetic algorithm (GA) so as to improve the global search ability and convergence speed of the algorithm; secondly, optimizing the structure and parameters of a deep neural network DNN by using the optimized BFA algorithm, and constructing a deep neural network model based on double hidden layers; according to the model, an attention mechanism and a residual block are introduced, so that the nonlinear mapping capability and the generalization capability are improved, and model overfitting is effectively prevented. In addition, the flight data and the weather condition data are combined, so that the prediction accuracy is further improved. The performance of the model is evaluated through multiple indexes, the result shows that the method can effectively solve the problem of DNN structure and parameter selection, and the training efficiency and generalization ability of the model are remarkably improved.

The invention relates to a quality control classification method and system suitable for laryngeal knot DR images, and the method comprises the steps: carrying out the preprocessing of each obtained historical laryngeal knot DR image, and obtaining a preprocessed image; dividing a training set, a verification set and a test set according to a preset distribution proportion based on the preprocessed historical laryngeal knot DR image set; the training set and the verification set are input into a classification network model for model training, the classification network model is composed of a plurality of cascaded residual blocks, and after output of each residual block, an attention and multi-scale aggregation module is integrated, so that an attention and multi-scale aggregation model is formed; the attention and multi-scale aggregation module is composed of a CBAM space channel attention module and an MSAA multi-scale feature aggregation module which are arranged in sequence; inputting the test set into the trained classification network model, and optimizing model parameters based on a model performance evaluation result; and acquiring a real-time laryngeal knot DR image, inputting the real-time laryngeal knot DR image into the trained and optimized classification network model, and processing the real-time laryngeal knot DR image to obtain a corresponding classification result.

This invention discloses a deep learning-based Hadamard transform-based anti-screen-capture watermarking method, belonging to the field of watermarking technology. This invention combines a convolutional neural network (CNN) and residual blocks to achieve an end-to-end process of watermark embedding and extraction within the Hadamard domain. By simulating screen-capture attacks and incorporating this method between the embedding and extraction layers, the network ensures robust watermark embedding. This method can efficiently extract watermark information from photos taken by surreptitiously, protecting the copyright of digital media. The use of the transform domain for watermark embedding allows the watermark to spread over a wider area of ​​the image, significantly improving the robustness of the watermarking algorithm. Comparison with related technologies demonstrates the superiority of this invention in terms of imperceptibility and robustness.

This invention discloses an adaptive edge-aware 3D medical image segmentation method, with the following specific steps: S1, constructing an adaptive edge-aware network, which includes an encoder and a decoder, with a skip connection between the encoder and decoder; S2, acquiring and processing a 3D medical image; S3, inputting the preprocessed image from step S2 into the encoder of the adaptive edge-aware network through a patch partitioning layer, then into the decoder through residual blocks and adaptive weight matching blocks. The decoder output and the original input image are skip-connected through adaptive weight matching blocks, and finally, the image segmentation result is output through residual blocks and Fourier convolution. This invention exhibits stronger robustness and boundary accuracy in multi-organ 3D segmentation tasks, providing an efficient and scalable solution for medical image segmentation.

Disclosed in the present application are an encoding method, a decoding method, a bitstream, an encoder, a decoder, and a storage medium. The method comprises: decoding a bitstream, so as to determine the value of first syntax identifier information; when the first syntax identifier information indicates that a multi-transform combination mode is applied to the current block, determining at least two first transform coefficients of the current block; determining at least two transform cores of the current block, and performing inverse transform on the at least two first transform coefficients respectively on the basis of the at least two transform cores, so as to determine at least two first residual blocks of the current block; and determining a reconstructed block of the current block on the basis of the at least two first residual blocks. In this way, the processing efficiency can be improved, thereby improving the encoding and decoding performance.

This invention discloses a dynamic obstacle avoidance method for assistive visually impaired scenarios. First, a feature extraction module based on a hierarchical residual structure constructed within residual blocks is built, which expands the receptive field of deep features. Second, a spatial feature recovery module based on bilinear interpolation and transposed convolution upsampling is designed to make segmentation edges more accurate. Third, a discrete sampling strategy is used to extract obstacle category, distance, and contour information, and path planning is performed using a heuristic search algorithm considering safe distance constraints. Finally, priority rules and logical order rules for the obstacle avoidance warning system are assumed, and a decision is generated based on the planned path, effectively guiding blind users to avoid obstacles through auditory and tactile information. This invention solves the problem that single-stage instance segmentation algorithms struggle to simultaneously and accurately segment small target obstacles and background road surfaces, overcoming the difficulty of providing effective information for obstacle avoidance decisions in assistive visually impaired scenarios with instance segmentation results, enabling blind users to avoid obstacles more intelligently and autonomously.

Disclosed are a video signal processing method and device whereby a video signal is encoded or decoded. The video signal processing method may comprise the steps of: determining whether an intra sub-partition (ISP) mode is applicable to a current block; if the ISP mode is applicable to the current block, splitting the current block into a plurality of rectangular transform blocks in the horizontal or vertical direction; generating prediction blocks of the transform blocks by carrying out intra prediction on each of the transform blocks; and reconstructing the current block on the basis of residual blocks of the transform blocks, and the prediction blocks.

The various implementations described herein include methods and systems for coding video. In one aspect, a method of video decoding includes receiving video data including a first block and a syntax element, from a video bitstream, where the syntax element is signaled at a first processing unit level. The method further includes selecting a transform coding mode based on the syntax element and performing a transform process for the first block using the selected transform coding mode, where the transform process is performed on a transform block at a second processing unit level, and where the second processing unit level is not signaled (e.g., is inferred from coded information).

This invention provides a weight training method and system for generating geological lithological texture models based on LoCon, belonging to the field of geological lithological texture generation technology. It employs an LDM model as the large model framework for weight training and texture generation. Convolutional layers are integrated into the convolutional weights of the residual blocks in the U-Net model within the LoCon model. The low-rank matrix of the LoCon model is added to the pre-trained weight matrix in the U-Net model to construct the generated geological lithological texture model. The model is trained using a training set. The trained weights are output, loaded into the large model framework for inference, and the training parameters are adjusted based on the inference results. Training is repeated until the preset inference result requirements are met. An API is provided to create an interface between the LDM large model framework and the trained weight matrix. This fills a gap in the geological industry's material library and lowers the technical threshold for geologists to participate in digitization.

Various implementations described herein include methods and systems for video coding and decoding. In one aspect, a method includes receiving a video bitstream, the video bitstream including a set of inter-mode coding blocks and a corresponding set of transform coefficients. The method includes deriving a set of inter-mode residual blocks from a set of transform coefficients. The method includes determining whether to apply one or more inseparable transform kernels to an inter-mode residual block set according to a value of a first indicator in a video bitstream. The method includes applying a first inseparable transform kernel when the indicator has a first value, and abandoning application of the first inseparable transform kernel when the indicator has a second value. The method further includes reconstructing the set of video blocks using the set of inter-mode residual blocks and the corresponding set of prediction blocks.

This invention discloses a cross-domain fault diagnosis method for bearings, aiming to address the problem of insufficient diagnostic generalization caused by significant differences in the distribution of bearing fault features and the scarcity of target domain labels under varying operating conditions. The method first constructs a multimodal input including a standardized one-dimensional time-series signal, a short-time Fourier transform time-frequency plot, and a phase space reconstruction plot. It then extracts temporal dependencies, time-frequency textures, and phase space topological features in parallel through a three-branch network consisting of a temporal convolutional network, residual blocks, and an inverse residual layer. These features are dynamically fused using a Transformer encoder to generate global features. High-confidence pseudo-labels are generated based on a momentum prototype distance metric, and class-level domain alignment is achieved by combining a homoscedastic uncertainty weighted composite loss function. Finally, iterative training outputs the target domain fault diagnosis results. Experiments show that the method achieves cross-domain diagnosis accuracy of 98.04% and 99.79% on the bearing datasets from the University of Paderborn and Case Western Reserve University, respectively, demonstrating both high accuracy and strong generalization, making it suitable for bearing fault diagnosis in industrial variable operating conditions.