182 results about "Diffusion network" patented technology

The invention discloses an industrial part defect sample accurate generation method based on a conditional diffusion model, and belongs to the field of image processing and artificial intelligence. The method forms a closed-loop cooperative system by constructing four deep coupling modules of physical constraint noise scheduling, multi-scale feature coupling, double-domain feedback optimization and adaptive weight adjustment; a defect physical forming mechanism is converted into a dynamic noise scheduling strategy, deep interaction between condition information and a feature map is established at multiple levels of a diffusion network, quality closed-loop optimization is achieved through dual evaluation of a pixel domain and a frequency domain, and training weight is dynamically adjusted according to defect scarcity. And multi-scale accurate control is realized, a quality guarantee closed loop is established, the problem of data imbalance is effectively solved, and the performance of an industrial defect detection model is remarkably improved.

The invention discloses an overlapped cervical cytoplasm region segmentation method based on deep learning and a conditional diffusion model, and relates to the technical field of artificial intelligence analysis of medical images. According to the method, accurate segmentation of the overlapped cytoplasm region in the cervical cell image is realized through a morphological prior guided conditional diffusion process. The method comprises the following steps: constructing a multi-scale cervical cytoplasm mask pair image; designing a cytoplasm specific data enhancement and preprocessing process; building a multi-branch cervical cell morphology perception condition diffusion network; using a self-adaptive multi-scale combination loss function to optimize training; and a hierarchical classifier is adopted to freely guide sampling for reasoning. According to the method, the frequency domain and space domain features are fused, a cellular morphology and statistics priori knowledge base is established, and a strategy of generating complete cytoplasm by adopting non-overlapped parts is adopted, so that the problem that the traditional method is difficult to segment in complex backgrounds and overlapped regions is successfully solved, and reliable technical support is provided for early screening of cervical cancer.

The invention discloses a power distribution terminal equipment fault intelligent diagnosis method and system based on the Internet of Things. The method comprises the following steps: S1, collecting multiple items of operation data of a power distribution terminal to construct a time sequence sample; s2, extracting power disturbance characteristics based on a sliding window, generating a behavior coupling matrix and a topological connection matrix, and calculating node redundancy; s3, fusing the two types of relationships to construct a dynamic graph; s4, inputting a graph diffusion network, fusing a structure and a behavior diffusion result, and generating a node state feature vector; s5, inputting a multi-label classification model to identify the fault type and probability; s6, generating a response instruction in combination with the fault type and the redundancy; s7, response is executed, the relation matrix and the classification model are updated, and the diagnosis process is optimized in a closed-loop mode. According to the invention, accurate fault identification and quick response of the power distribution terminal are realized, and the intelligent operation and maintenance level of a power supply system is improved.

The invention discloses a metering processing method for an electric energy meter topology application and an electric energy meter of a transformer area topology. The metering processing method comprises the following steps: S1, constructing a measurement data set based on an equipment identifier, a power factor and a sampling timestamp of the electric energy meter; s2, generating an initial graph model containing user nodes and electric energy transmission edges based on the data set; s3, inputting a dynamic coupling backflow enhancement graph diffusion network to execute topology identification; s4, constructing a fusion path diagram and initializing an edge weight; s5, calculating an edge weight regulation factor, and updating the edge weight; s6, asynchronous propagation is carried out to output a transformer area power supply topological graph; s7, constructing a metering path based on the topological graph and identifying an abnormal path segment; and S8, correcting the abnormal path, and generating an optimization topology and evaluation report. According to the method, the precise identification of the power supply topology of the transformer area and the self-adaptive correction of the abnormal metering path are realized.

The invention discloses a remote sensing image super-resolution reconstruction method and device, a storage medium and equipment, relates to the technical field of computer vision, and can solve the technical problem that the remote sensing image super-resolution reconstruction effect is poor. The method comprises the following steps: acquiring a sample remote sensing image and multi-modal auxiliary data, extracting spectral features, textural features and geometric features of the pre-processed multi-modal auxiliary data, and fusing to generate a condition feature tensor; performing forward diffusion on the sample remote sensing image to generate a noise-containing image sequence; in the process of training the UNet diffusion network by using the noisy image sequence, injecting the conditional feature tensor into preset levels of an encoder and a decoder of the UNet diffusion network, and optimizing network parameters through multi-objective loss to obtain a trained UNet diffusion network; and obtaining a to-be-reconstructed noise map, performing noise prediction on the noise map based on the trained UNet diffusion network to obtain target noise, performing reverse sampling on the noise map, and generating a target reconstructed image through iterative denoising.

Approaches presented herein provide for the generation of synthesized data from input noise using a denoising diffusion network. A higher order differential equation solver can be used for the denoising process, with one or more higher-order terms being distilled into one or more separate efficient neural networks. A separate, efficient neural network can be called together with a primary denoising model at inference time without significant loss in sampling efficiency. The separate neural network can provide information about the curvature (or other higher-order term) of the differential equation, representing a denoising trajectory, that can be used by the primary diffusion network to denoise the image using fewer denoising iterations.

The invention discloses a face image generation method and device, a storage medium and electronic equipment. The method comprises the following steps: determining k-th scene attribute description information from scene attribute description tags corresponding to a target scene; extracting a kth scene text feature from the kth scene attribute description information; performing denoising processing on the initialized noise image by using the kth scene text feature in the constraint diffusion network to generate a kth denoised face image; under the condition that k is not equal to P, de-noising processing is performed on the initialized noise image by using the (k + 1) th scene text feature in the constraint diffusion network, and a (k + 1) th de-noised face image is generated; under the condition that k is equal to P, P denoised face images are added to the face image set, and P is the maximum combination number of the scene attribute description labels. According to the method and the device, the technical problem that intra-class consistency of a generation result is difficult to ensure in a face image generation mode provided by a related technology is solved.

The invention provides an unbiased missing modal learning method based on a multi-stage double diffusion network, and belongs to the field of computer vision, natural language processing and multi-modal information fusion. The method comprises the following steps: a multi-modal feature extraction module maps text modal data, image modal data and audio modal data into a unified potential representation space; the potential space representation of the three modals is used as the original feature of the missing modality and the original feature of the available modality; based on the missing modal original features and the available modal original features, training a multi-stage double-diffusion module through forward diffusion and reverse diffusion to obtain a trained multi-stage double-diffusion module, and based on the trained multi-stage double-diffusion module, performing global structure generation and modal conversion through available modal data to obtain a multi-stage double-diffusion model; and performing local detail optimization reasoning to generate missing modal data. According to the method, the problem of modal generation deviation in the existing multi-modal learning is solved, and the effectiveness of the multi-modal learning effect in the missing scene is enhanced.

The invention provides a method for predicting a cabin material pile in a ship loading process of a ship loader based on a diffusion network, and relates to the technical field of bulk cargo loading and unloading automation. Three-dimensional point cloud data in a cabin in the ship loading process is acquired, the aligned three-dimensional point cloud data is projected to a two-dimensional plane parallel to the bottom of the cabin, and a material pile depth map is generated; constructing a training sample pair based on the historical ship loading operation data, and training a conditional diffusion network model to obtain a target conditional diffusion network model; inputting the material pile depth map of the first time period and planned blanking point information corresponding to the second time period into a target condition diffusion network model to obtain a material pile depth map corresponding to the second time period; and taking the material pile depth map corresponding to the second time period as an initial depth map for prediction in the next time period to carry out iterative prediction, and outputting a material pile depth prediction set after the material pile depth map prediction in all time periods in the ship loading process is completed. According to the invention, the fidelity and authenticity of the predicted material form can be improved.

The invention relates to the field of cross-modal information retrieval, in particular to a text video cross-modal retrieval method and system based on two-stage neighborhood diffusion, and the method comprises the steps: carrying out the feature extraction of a to-be-retrieved text and a to-be-retrieved video, and obtaining the global feature vectors of the to-be-retrieved text and the to-be-retrieved video; calculating random neighborhoods corresponding to the text and the video based on the cosine similarity between the to-be-retrieved text and the to-be-retrieved video; performing random sampling on the global feature vector of the text / video to be retrieved in the corresponding random neighborhood to obtain random neighborhood text embedding and random neighborhood video embedding so as to perform embedding space modal alignment of the text and the video; tensor splicing is carried out on random neighborhood embedding of the to-be-retrieved text and the to-be-retrieved video, and cross-modal data distribution of the text and the video is optimized based on a pre-constructed dynamic agency attention diffusion network. Through random neighborhood modeling and a dynamic agency attention diffusion network, the cross-modal alignment precision and the model generalization ability are improved.

Systems and methods for generating 3D scenes include a masked red, green, blue, depth (RGBD) input, which is separated into a masked RGB input and a masked depth input. The masked depth input is compressed. The masked RGB input is compressed. A high definition (HD) map control signal is generated for a depth stream, and an HD map control signal is generated for an RGB stream. A depth output is generated based on inputs from the depth stream, the HD map control signal for the depth stream, text encoder, and random sampled noise. An RGB output is generated based on inputs from the RGB stream, the HD map control signal for an RGB stream, text encoder, and random sampled noise to train a dual stream diffusion network.

The embodiment of the invention provides an image restoration method and device based on a diffusion model and generative adversarial training, and the method comprises the steps: obtaining image data, inputting the image data into a degeneration encoder, and obtaining a first result; providing an interaction page, and obtaining image limitation information based on the interaction page; the first result and the image limiting information are input into an image restoration network to obtain a second result, and the image restoration network is generated based on a diffusion model in a pre-trained diffusion network; inputting the second result into an image decoder for processing to obtain a restoration result, and feeding back the restoration result; according to the scheme, the pre-trained encoder and the diffusion model can be finely adjusted so as to be suitable for restoration of the degraded image, and a more accurate restored image can be obtained.

The invention discloses a background coherent story picture book generation method based on a diffusion model, and belongs to the field of computer vision and generative artificial intelligence. The method comprises a training stage and a testing stage: in the training stage, bidirectional cross attention fusion and modal soft selection are carried out on text, background and role multi-modal conditions through a feature enhancement fusion module, model optimization is carried out by utilizing joint alignment loss, and efficient parameter fine tuning is carried out on a diffusion model by adopting an efficient parameter fine tuning method; in the test stage, a reference image input by a user and a text sequence are processed into a fusion condition, a large language model is driven to generate an image mark, the image mark is converted into a diffusion condition through a mapper, and each frame of image is generated step by step by combining an autoregression mode with a multi-condition injection diffusion network. According to the method, the problem of inconsistency of cross-frame backgrounds, styles and roles is effectively solved, and high-quality and coherent generation of the long-sequence story picture book is realized.

A computer-implemented system for foreign object detection in a scene. The system includes a memory-suppress diffusion network module adapted to reconstruct a reconstructed image from an encoded image, and a contrastive dissimilarity network adapted to combine the input image and the reconstructed image to predict an anomaly map for the input image. The encoded image is based on an input image, and the memory-suppress diffusion network module and the contrastive dissimilarity network are trained using only normal, real images. The system leverages only normal images in training and does not compromise the detection performance at the inference stage.

The invention relates to the technical field of video super-resolution reconstruction, in particular to an unmanned aerial vehicle video super-resolution reconstruction method and system based on an implicit diffusion model, and the method comprises the steps: encoding a low-resolution frame into a low-dimensional latent feature through an implicit neural expression auto-encoder; denoising the latent features of the reference frame through an implicit neural U-shaped diffusion network to obtain clean latent features, generating a high-resolution reference image through an implicit neural decoder, and performing optical flow alignment to obtain a time consistency control signal; and finally, inputting the current frame latent features and the control signal into an implicit neural U-shaped diffusion network with a historical sampling correction module, and outputting a super-resolution image of any scale by an implicit neural decoder after iterative denoising. According to the method, in the unmanned aerial vehicle video super-resolution reconstruction task, any-scale reconstruction is realized, the problems of space-time inconsistency and sampling error accumulation are effectively relieved, and the method has better performance in the aspects of perception quality, time sequence consistency and the like.

The invention provides an MIG fragment rearrangement method and system based on topology awareness and a double-layer agent, and belongs to the technical field of artificial intelligence. The method comprises the steps of collecting calculation of each partition and video memory occupation and remaining execution time, generating partition embedding through Kronecker multiplication, Hadamard product and PCIe neighborhood aggregation, constructing an address adjacency matrix, generating a global condition vector through Sigmoid gating and fusion pooling, and inputting a diffusion network to generate a fragment evolution trajectory and confidence. And calculating continuous splicable capacity based on confidence, constructing a graph structure, evaluating candidate migration actions by a lightweight graph strategy network, and selecting an optimal sequence through risk tensor and Bayesian calibration iterative search. And in the execution stage, the migration duration is controlled through an NVML lock table, RDMA straight pulling and a sliding window, and the device tree is updated after the migration duration is completed. Real evolution is continuously collected on line, deviation is monitored through divergence, the model is updated through distillation, and the optimal action and pause duration are recorded through an experience pool.

The invention provides a 4D millimeter wave radar and camera information fusion image enhancement method and system based on deep learning, and relates to the technical field of image enhancement. According to the method, a confidence coefficient matrix is generated through space-time registration, micro projection and fuzzy C-Means-ICP fusion, a radar feature body guided by fuzzy weight is constructed, cross-modal attention fusion and self-supervised optimization are realized in a convolution-converter-diffusion network, and the image enhancement quality and robustness in a complex environment are effectively improved.

Systems and methods for generating 3D scenes include a masked red, green, blue, depth (RGBD) input, which is separated (1602) into a masked RGB input and a masked depth input. The masked depth input is compressed (1604). The masked RGB input is compressed (1606). A high definition (HD) map control signal is generated (1608) for a depth stream, and an HD map control signal is generated (1610) for an RGB stream. A depth output is generated (1616) based on inputs from the depth stream, the HD map control signal for the depth stream, text encoder, and random sampled noise. An RGB output is generated (1618) based on inputs from the RGB stream, the HD map control signal for an RGB stream, text encoder, and random sampled noise to train a dual stream diffusion network.

The invention belongs to the technical field of computers, and provides a video virtual fitting method and device based on a diffusion model and a program product. The method comprises the following steps: firstly, acquiring a target clothing graph and a model video, and extracting depth features, masks and a model latent feature video; meanwhile, semantic codes of the clothes are extracted through a CLIP encoder. The first diffusion model processes the clothing graph and the CLIP code, and outputs a clothing space attention feature vector fusing fine-grained texture and semantics; and the second diffusion model serves as a video diffusion network, receives random noise, character features, masks and depth features, injects garment vectors and CLIP codes into the middle layer, and finally outputs a fitting video with naturally fitted and dynamically coherent garments. According to the method, the high-fidelity virtual fitting video can be efficiently generated according to the new clothing and model video, and the method has wide application value in the fields of electronic commerce and the like.

Approaches presented herein provide for the generation of synthesized data from input noise using a denoising diffusion network. A higher order differential equation solver can be used for the denoising process, with one or more higher-order terms being distilled into one or more separate efficient neural networks. A separate, efficient neural network can be called together with a primary denoising model at inference time without significant loss in sampling efficiency. The separate neural network can provide information about the curvature (or other higher-order term) of the differential equation, representing a denoising trajectory, that can be used by the primary diffusion network to denoise the image using fewer denoising iterations.

The invention discloses a human body posture estimation result generation method and device based on a generative model, relates to the technical field of image processing, effectively recovers a complete skeleton structure, realizes accurate recognition of human body postures in high-shielding and high-dynamic change scenes such as an electric power production field, and improves the adaptability to shielding scenes. The method comprises the following steps: acquiring a to-be-identified video stream, and performing initial attitude estimation on each frame of video image in the to-be-identified video stream by using a pre-trained attitude detection network model to obtain an initial skeleton point sequence; fitting the initial skeleton point sequence by adopting a Gaussian mixture modeling method to obtain a defect skeleton point sequence; inputting the defective skeleton point sequence into a pre-trained inverse diffusion network model, and optimizing the defective skeleton point sequence in combination with the adjacent frame skeleton point coding information of each frame of video image in the inverse diffusion network model to obtain a complete skeleton point sequence; and generating and outputting a human body posture estimation result based on the complete skeleton point sequence.

Provided is a target model training method, a multimodal data processing method, and devices therefor, relating to the field of artificial intelligence technology, and in particular to the fields of computer vision, deep learning, large model and other technologies. The target model training method includes: inputting sample data into a preset model to obtain initial multimodal features of the sample data; and using the initial multimodal features and a preset noise feature to perform model training on N diffusion networks in the preset model to obtain a target model when parameters of an image-text encoder of the preset model are fixed.

The invention discloses an airport runway foreign object detection (FOD) radar echo noise reduction and classification method, and the method comprises the steps: constructing a fusion network which comprises a conditional generative adversarial network, a diffusion network and an entropy evaluation mechanism for promoting the game symmetry of the generative adversarial network, and introducing block discrimination and overall constraint into the generative adversarial network, a diffusion network is constructed at the rear end of the generative adversarial network to further denoise the output data and improve the signal-to-noise ratio, an information feedback path from a discriminator to a generator is also established, and the real sense of the detail texture and the overall structure of the generated data is improved. The generator can concentrate the training center of gravity in an uncertain area of the discriminator according to the feedback of the discriminator, so that the learning ability of the generator in an easily confused area is enhanced, and meanwhile, the output of the discriminator is pushed to be close to the maximum entropy state to realize game symmetry; according to the technical scheme, noise can be removed, the target feature information in the radar echo data can be accurately extracted, and the classification accuracy is improved.

The invention discloses a two-stage potential diffusion system and method for high-fidelity virtual fitting, and the method comprises the steps: firstly building a semantic corresponding relation between a garment and a human body in a potential space through a first diffusion network, and generating a deformed garment adaptive to a target posture in combination with an input garment image, a human body image and an optional mask image; and then, introducing a cross-modal fusion network based on conditional diffusion, taking the VAE codes of the deformed garment and the original garment image generated in the first stage and the VAE code of the target figure image as input, and finally synthesizing a high-quality virtual fitting image through multi-scale feature fusion and integration of a semantic structure, original garment texture and a human body posture. According to the invention, through a two-stage progressive generation strategy and a specific network structure, challenges of an existing virtual try-on method in the aspects of detail reservation, semantic alignment, training stability, generation quality and the like are solved, and a high-fidelity virtual try-on effect with rich details, vivid textures and matched postures can be generated.

The invention relates to the technical field of public health information, in particular to an infectious disease transmission path generation and early warning method and system based on big data, and the method comprises the steps: collecting and cleaning multi-modal data, and obtaining a pulse event flow through neuromorphic coding; and constructing a variable order hyperedge graph in a second-level window, extracting loop life to adjust diffusion of the cellular automaton, and iteratively generating an infection prediction field. And then splicing the hyperedge graph, the prediction field and the topological fingerprint into a conditional tensor, and inputting the conditional tensor into a conditional score diffusion network to generate a future propagation event sequence. And constructing a tensor network according to the sequence, mapping the tensor network into Isin Hamiltonian, quickly selecting an intervention node through an optical delay ring reservoir and gradient descent, and issuing a ventilation or purification instruction. And after a virus load difference value before and after intervention is normalized, a tensor network, a diffusion network and a cellular automaton are synchronously updated to form a self-adaptive closed loop, so that a peak time error can be shortened, and the recall rate of a high-risk region is improved.

The invention discloses an infrared small target detection method based on visual thermal diffusion and selective multi-scale feature fusion, and mainly solves the problems of easy feature loss and low detection precision caused by weak target and complex background in the existing infrared small target detection technology. The implementation scheme is as follows: 1) acquiring a data set and a segmentation label; 2) constructing an infrared small target detection model; 3) constructing a loss function; 4) training an infrared small target detection model; and 5) obtaining an infrared small target detection result. According to the infrared small target detection model constructed by the invention, through a diffusion network encoder pre-trained based on a visual thermal diffusion principle, feature representation of a weak target is enhanced from the source; intelligent fusion and enhancement of multi-level features of the encoder are realized through a selective dimension fusion module deployed on jump connection; and through a selective cavity channel refining module and a selective receptive field fusion module which are embedded in the network, the high efficiency of the whole architecture is ensured while the fine extraction capability of the network to small targets is improved.

The invention discloses a quantitative prediction method for the scouring rate of an offshore wind power pile foundation based on machine learning, and the method comprises the steps: collecting historical multi-source topographic observation data, and generating a topographic spatio-temporal evolution graph; collecting tidal current sediment dynamic field data to obtain calibrated tidal current sediment dynamic field data; constructing a learnable terrain disturbance mapper, and outputting a seabed digital elevation field at a target prediction moment; constructing a physically-driven attention guidance dual-channel diffusion network; respectively obtaining a local scouring rate prediction field and an overall state evolution rate prediction field; outputting the scouring rate prediction result data and the scouring depth prediction result data of the surrounding area of the pile foundation; generating updated scouring rate prediction result data and updated scouring depth prediction result data; and a pile foundation collaborative protection scheme is automatically generated. According to the method, collaborative protection optimization design of local scouring and overall scouring of the offshore wind power pile foundation is achieved, and the protection effect and economic benefits are remarkably improved.

The application discloses a medical image segmentation method based on a pre-training diffusion model, which comprises the following steps: 1, obtaining a medical image segmentation dataset and preprocessing; 2, establishing a forward noise adding, reverse noise removing process and a pre-training prediction network; and 3, training a segmentation network and a prediction. The application directly predicts an original label and prior knowledge of a large model through a diffusion network, and improves the segmentation performance of a target region in a medical image.

The invention provides a spatial neural network depth completion method, system and device based on an adaptive diffusion kernel, and a storage medium, and the method comprises the following steps: obtaining an RGB image and a sparse depth speckle image, and collecting the RGB image and the sparse depth speckle image through an RGB camera and a depth camera respectively; a pre-trained deep completion model is obtained, the deep completion model comprises a U-shaped network model and a diffusion network model, and the diffusion network model comprises a plurality of preset diffusion patterns; generating an adjacent pixel similar matrix corresponding to each pixel in the sparse depth speckle image for the input RGB image and the sparse depth speckle image through a U-shaped network model, and selecting a corresponding diffusion image for each pixel of the sparse depth speckle image according to an adjacent pixel similar matrix root through the diffusion network model, and performing depth completion to generate a dense depth speckle image. According to the method, the depth completion precision can be remarkably improved, and the calculation speed is remarkably improved during depth completion.

The present application relates to a method and device for implementing image offline rendering based on Diffusion, and a storage medium, applied to the technical field of image processing, comprising: after obtaining the rendering scene and final rendering parameters of a user, adjusting the final rendering parameters to preset template rendering parameters, the template rendering parameters can achieve fast rendering while retaining as much information in the 3D scene as possible, then rendering the scene through the template rendering parameters, so that a low-quality initial rendering image can be quickly obtained, then introducing a pre-trained Diffusion network model, enhancing the initial rendering image through the pre-trained Diffusion network model in comparison with the final rendering parameters, and obtaining the final rendering image through multiple iterations, so that a high-quality rendering image meeting the user's requirements can be obtained, and a lot of time is not spent in the offline rendering process.