7results about How to "Reduce training complexity" patented technology

This application provides a neural network operation method, apparatus, and storage medium, including: adding a random mask layer to a first neural network according to the type of variable dimensions to obtain a second neural network; the random mask layer is used to perform random masking processing on tensors input to the random mask layer; and training the second neural network using sample data. The neural network operation method, apparatus, and storage medium provided in this application, by adding a random mask layer to the neural network, only require training and deployment of one neural network to adapt to different input and output dimension requirements. This method has low training complexity, low storage overhead, and is easy to deploy and continuously evolve through online training.

The invention belongs to the field of computer vision and artificial intelligence, and discloses a salient object detection method based on a diffusion model and semantic guidance. Comprising the following steps: automatically generating text annotations of a saliency object data set; constructing a cross-modal saliency pseudo mask; carrying out structure optimization on an initial pseudo mask; constructing a saliency object detection framework based on a diffusion model; and training and testing the salient object detection model based on the diffusion model and the semantic guidance by using the salient object data set. According to the method, the saliency segmentation result can be effectively recovered, verification is carried out on a plurality of public data sets, and the saliency detection precision and the boundary integrity can be remarkably improved.

The invention provides a direct shear test soil parameter calibration method fusing numerical simulation and machine learning, and relates to the field of geotechnical engineering. According to the direct shear test soil parameter calibration method fusing numerical simulation and machine learning, high-fidelity finite element numerical simulation, Kernel PCA dimension reduction, a PINN agent model and Bayesian parameter inversion are subjected to systematic deep fusion for the first time. The progressive damage process from the edge to the interior and non-uniform stress-strain distribution of a soil body in a direct shear test are precisely restored through high-fidelity finite element numerical simulation, low-dimensional essential characteristics of a high-dimensional response curve are extracted through a dimension reduction method, and a constitutive model and a strength criterion are embedded into an agent model to guarantee physical rationality. And finally, parameter estimation is realized through a Bayesian parameter inversion framework. The combination solves the interference of non-uniform stress and strain of the soil body in the direct shear test on parameter calibration, so that the calibration result better fits the actual mechanical property of the soil body.

The invention provides a ponding image day and night bidirectional conversion model construction method based on reflection map consistency and application, and the method comprises the following steps: obtaining a plurality of groups of real day and night ponding image pairs comprising real daytime ponding images and real night ponding images, and generating daytime scene cues for each real daytime ponding image, generating a night scene prompt word for each real night ponding image; constructing a waterlogging image day and night bidirectional conversion framework comprising a brightening conversion unit and a darkening conversion unit, wherein the waterlogging image day and night bidirectional conversion framework generates a simulated night waterlogging image and a simulated daytime waterlogging image; and performing iterative training on the ponding image day and night bidirectional conversion architecture by using a plurality of groups of real day and night ponding images to obtain a ponding image day and night bidirectional conversion model. According to the scheme, the reflection consistency decoder is constructed to extract the reflection maps of various ponding images and calculate the reflection consistency loss, the conversion process is constrained without changing the essential attribute of the ponding area, and semantic distortion is effectively prevented.

The application belongs to the field of information security, and provides a high-robustness image tampering detection method, which comprises the following steps: 1) obtaining multi-scale feature extraction by using a hole convolution, and using Concat to obtain a global feature representation, so that the loss of shallow layer features after multiple convolutions can be prevented, and a larger receptive field can be obtained, and the effect of small target tampering detection is improved; 2) extracting noise by using BayarConv, and the noise extractor is better than an SRM filter, and Bayarconv is developed to enhance the noise inconsistency between the manipulated area and the real area in a given image; 3) using a residual block, batch normalization and an activation function to capture spatial features, and the structure is a conventional encoder, and the main purpose is to cross-fuse with semantic features; 4) using a cross-attention mechanism to fuse spatial features and semantic features, and the fused features are input into an up-sampling layer and a convolution layer for detection, and a hybrid loss function is used to train the model. Experimental data on a large number of public data sets show that the cross-attention mechanism provided in the application can not only accurately predict the tampering position, but also more accurately identify the tampering type.

The invention discloses a data expansion and exoskeleton joint end-to-end torque estimation method based on a diffusion model, and the method comprises the steps: carrying out the normalization processing of time series data from a multi-source sensor, carrying out the fragmentation according to a fixed length, and constructing a training sample with a motion class label; then, training a classifier-free conditional diffusion model by adopting a sample, and simultaneously learning conditional and unconditional denoising mapping relationships in a manner of randomly inactivating category conditions; in the generation stage, based on a classifier-free condition guidance mechanism, multi-modal time series data with specified motion category features are gradually generated from random noise; and finally, fusing the generated synthetic data with real acquired data to train a joint torque end-to-end prediction network, thereby realizing joint torque estimation of input sensor time sequence data. The method improves the prediction precision, generalization ability and stability of the end-to-end torque estimation model in a multi-action and few-sample scene, and has a good engineering application value.

This invention relates to a text-driven immersive large-scene neural rendering and blending enhancement method, comprising: 1. Creating a dataset; 2. Reconstructing the large scene based on an improved progressive neural radiation field; 3. Predicting the foreground and background matte values ​​of the rendered image based on a convolutional neural network; 4. Generating the background from text using a stable diffusion model; 5. Calculating background motion through changes in camera parameters between adjacent frames; 6. Blending the rendered foreground and background and coordinating lighting. This invention achieves real-time text-driven, editable large-scene background enhancement, allowing rendering of immersive large scenes at any observation position at a city scale, generating observation images with effects consistent with the real scene, and enabling mixed reality on this basis. It can satisfy users' personalized customization of the scene and achieve visual effects at the level of film and television special effects. This technology can be applied to fields such as 3D visualization, digital mapping, and virtual reality games.