2114 results about "Coding decoding" patented technology

The invention discloses a video semantic segmentation method based on a time sequence cross attention mechanism, and belongs to the field of computer vision and the field of material detection.The video semantic segmentation method comprises the steps that firstly, a video used for training is preprocessed, a frame sequence is extracted, and then a coding-decoding network for multi-level feature extraction and fusion is constructed; according to the method, feature extraction is enhanced through a time sequence cross attention module, network parameters are optimized through weighted IoU loss and binary cross entropy BCE loss, then frame-by-frame prediction segmentation is carried out on a target video by using a trained model, and a multi-classification segmentation result is exported. According to the method, a time sequence cross attention mechanism is integrated into the SAMUNet network, the segmentation precision is effectively improved for image data with time sequences, the time cost and the labor cost of material video processing are greatly reduced, the method can be widely applied to the field of industrial detection, and the product quality and the production efficiency are improved.

The invention provides a multi-modal automatic driving scene generation method based on autoregressive closed-loop prediction. The method comprises the following steps: encoding an automatic driving scene video, scene understanding prediction questions and answers and trajectory planning data into a unified multi-modal discrete code by using a pre-trained discrete encoder; multi-modal discrete codes are integrated into a coding sequence, a mask strategy is adopted to cover trajectory codes, original codes without masks serve as a supervision target, a model is generated through autoregressive normal form training, and fine tuning is carried out on scene understanding, scene prediction and trajectory planning data sets; during reasoning, an initial scene image collected by a vehicle camera and a user prompt word are input, the model generates a future scene discrete coding sequence according to initial information, and closed-loop prediction is realized through mask track information; the generated codes are decoded into scene images, text questions and answers, and vehicle trajectories using a symmetric decoder. According to the invention, the problems of poor universality and low efficiency of the existing end-to-end automatic driving world model can be solved.

The invention relates to the technical field of commodity anti-counterfeiting, in particular to an intelligent identification and analysis method for an anti-counterfeiting mark, which comprises the following steps: firstly, acquiring environmental parameters in real time, acquiring a holographic image of the anti-counterfeiting mark through a multispectral imaging technology, and correcting the image by using environmental data to generate a standardized hologram; then microcosmic anti-counterfeiting features in the dynamic dot matrix hologram are extracted to construct holographic feature vectors; and synchronously collecting encrypted bar code data, dynamically adjusting an error correction parameter and a decoding threshold value of a bar code decoding algorithm through the environment compensation model, and obtaining a first optimized bar code decoding algorithm for decryption. And checking the aging matching between the hologram timestamp and the bar code dynamic key by using an asymmetric encryption algorithm, and calculating the matching degree between the real-time holographic feature vector and a pre-stored standard vector by using a feature similarity algorithm. And when the matching degree is greater than a preset threshold value and the dynamic key verification is passed, determining that the product is a genuine product and updating the dynamic key.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

Disclosed herein are a method, an apparatus and a storage medium for image encoding / decoding. In typical image encoding / decoding methods, a decoder-side motion information derivation method may be limitedly used. Therefore, the improvement of encoding efficiency attributable to the decoder-side motion information derivation method may also be limited. In embodiments, a motion information search method used in an inter-prediction mode, an intra block copy mode and an intra template matching prediction mode is disclosed. With the use of various motion search methods, encoding efficiency may be improved.

An image encoding / decoding method and device according to the present disclosure can induce an intra-prediction mode of the current block on the basis of an induction mode, determine a reference sample of the current block, and generate a prediction sample of the current block on the basis of the intra-prediction mode and the reference sample.

Disclosed herein are a method, an apparatus and a storage medium for image encoding / decoding. Methods for performing more accurate prediction are disclosed when performing prediction using a block vector. A block vector for a target block is derived by template matching, and a prediction block for the target block is determined based on the derived block vector. Various and detailed embodiments for a plurality of pieces of information used to derive a block vector, such as a search area, a block vector candidate list, a reference region, a buffer, and a tree type, are provided.

The invention relates to a machine tool thermal error modeling method based on a time convolution network and transfer learning, which comprises the following steps: data acquisition: providing a network based on an encoder-decoder architecture based on the time convolution network, inputting the acquired data of source (sufficient) and target (few) machine tools into an encoder, and carrying out data processing on the encoder; the method comprises the following steps of: coding and decoding to output a reconstruction sequence, and adding a reconstruction loss function which is calculated by the reconstruction sequence output by a decoder on a loss function with only one predicted value, so that deep features in thermal error data can be extracted through the architecture, and the prediction precision is improved; in order to achieve enough prediction precision on another target machine tool, transfer learning is used to obtain a loss function adapted to a field, the loss functions are summarized, iterative updating is repeated until deep learning regression algorithm training is completed, and accurate prediction of the thermal error value on the target machine tool is realized.

A robotic controller for controlling a robot according to a sequence of robotic actions. comprises an input interface configured to receive a plurality of multimodal inputs each specifying instructions for performing a task in a different modality including audio, video, and a text modality. The controller also comprises a multimodal large language model, an action sequence decoder, and a controller. The multimodal LLM includes a multimodal LLM encoder and an LLM decoder. The multimodal LLM encoder is trained with machine learning to transform the multimodal instructions into encodings and the LLM decoder is configured to decode the encodings into a sequence of robotic instructions. The action sequence decoder is trained with machine learning to transform the sequence of robotic instructions into a sequence of actions using a library of robotic skills. The controller is configured to control a robot according to the sequence of actions.

This disclosure provides an image encoding / decoding method and device and recording medium storing a bitstream. The image decoding method may comprise: constructing a candidate list related to prediction of a current block; generating a prediction block of the current block based on a candidate selected from the candidate list; and generating a reconstructed block of the current block based on the prediction block, wherein the constructing of the candidate list includes adding candidates to the candidate list in an order of a first candidate serving as a spatially neighboring block, a second candidate serving as a temporally neighboring block, and a third candidate serving as a non-neighboring block.

The invention discloses a thunder approaching prediction method and device based on multi-source meteorological data, and particularly relates to the technical field of thunder disaster prediction.The method comprises the steps that standardization processing of temporal-spatial resolution unification is conducted on meteorological satellite data, radar data and lightning positioning data, and standardized temporal-spatial input data is generated; then spatial-temporal features are extracted through a depth separable 3D convolution module, and a compressed spatial-temporal feature graph is generated; then, a channel-space double attention mechanism (CPCA) is applied to the compressed feature map for feature optimization; and finally, processing the optimized feature map through a coding-decoding structure, and outputting a thunder and lightning probability distribution map. According to the method, the problem of spatial-temporal resolution difference and physical feature mismatching in multi-source data fusion is innovatively solved, the calculation efficiency is remarkably improved through a lightweight network architecture, the feature expression ability is enhanced by using an attention mechanism, and high-precision prediction of sudden thunder and lightning events is realized.

The invention belongs to the technical field of image processing and deep learning, and particularly relates to an image defogging method based on dynamic wavelet prior and double-domain learning. Aiming at the requirements of all-weather clear imaging in the fields of intelligent traffic systems, safety monitoring and the like, and in order to overcome the defect that a static convolution kernel adopted by a traditional defogging method is difficult to adapt to different haze degradation, the invention provides a method for dynamically generating a convolution kernel by using haze priori contained in a multi-scale wavelet LL sub-band; and an efficient, robust and accurate image defogging model is constructed. According to the invention, based on a multi-scale U-shaped coding-decoding architecture, a dynamic wavelet depth separable convolution module DyWConv is embedded in front of each level of a coder to realize content adaptive feature extraction, and a double-domain feature learning module SPAFormer Block cooperatively utilizing Fourier domain global modulation and wavelet domain multi-scale decomposition is designed. And double-domain features are fully fused through an adaptive gating fusion mechanism, and finally a clear image is reconstructed and output step by step. According to the method, a method for explicitly encoding frequency domain degradation prior into dynamic convolution kernel parameters is innovatively provided, the complementary advantages of Fourier transform and wavelet transform are cooperatively utilized, spatial non-uniform haze can be effectively removed, image details can be recovered, leading performance is achieved in a synthetic data set and a real scene, and the method has a wide application prospect.

The invention belongs to the technical field of communication, and particularly relates to a multiplication active code decoding method based on an SO-ORBGRAND decoder. The SO-ORBGRAND decoder outputs soft information by calculating a correct posterior probability of a conjecture code word sequence, and high-performance decoding is realized through soft information exchange and optimization processing between the row decoder and the column decoder. In the iteration process, a method for judging the validity of the code word through the check matrix is designed by utilizing the polarization characteristic of the multiplication active code, so that the iteration is supported to jump out in advance to reduce the complexity. The invention provides a multiplication active code decoding algorithm based on an SO-ORBGRAND decoder, and compared with various non-system multiplication active code decoding algorithms and the performance of traditional long-code polarization codes, the method has remarkable advantages in error correction performance; a method for judging the validity of the code word through the check matrix by utilizing the polarization characteristic design of the multiplication positive code is provided, the complexity is reduced while the judgment success rate is improved, and the implementation overhead is reduced.

The invention relates to the technical field of short-term load prediction in a power system, and discloses a short-term load prediction system based on a coder-decoder architecture, and the system is characterized in that a coder is used for extracting local features of a power load mode; and the decoder is used for converting the local features of the power load mode into predicted power load values and outputting the predicted power load values. The encoder is realized by a multi-scale expansion causal convolutional network MSDCC, and the decoder is realized by a bidirectional long short-term memory network BiLSTM. The prediction system construction method comprises the following steps: extracting related data from a historical database, preprocessing and analyzing the data, and constructing a predictor matrix; an MSDCC encoder is constructed; a BiLSTM decoder is constructed; and combining the encoder-decoder architecture to construct a short-term load prediction model. According to the method, the size of the feature map is effectively limited, model parameters are reduced, overfitting is avoided, calculation requirements are controlled, non-linear features are efficiently captured, meanwhile, time keeping complexity is low, and therefore the method has the advantages of being high in prediction efficiency and high in prediction precision.

An encoding method includes first obtaining a to-be-encoded picture block, then determining encoding complexity of the to-be-encoded picture block based on a plurality of prediction modes, next determining a quantization parameter of the to-be-encoded picture block based on the encoding complexity, and after that encoding the to-be-encoded picture block based on the quantization parameter to generate a bitstream. The plurality of prediction modes include at least one of block-level prediction, point-level prediction, and intra block copy prediction, and the encoding complexity indicates encoding difficulty of the to-be-encoded picture block.

A video coder may code a flag indicating whether or not a neural network (NN)-based intra prediction mode is enabled before coding a decoder side intra mode derivation (DIMD) flag, and code a block of video data based on whether or not the NN-based intra prediction mode is enabled.

The invention discloses a micro-service anomaly detection method and system based on time-frequency fusion. The method comprises the following steps: acquiring micro-service KPI data and preprocessing the data; an FTC model is constructed, the FTC model comprises a fast Fourier transform operation, a 2D-CNN, a 1D-CNN and two Transform encoder-CNN decoders, and the time domain features of the KPI are extracted from the preprocessed data through the 1D-CNN; meanwhile, after the preprocessed data is subjected to fast Fourier transform operation and 2D-CNN processing, information of a KPI frequency domain is extracted; the time domain features and the frequency domain features are coded and decoded through two parallel Transform encoder-CNN decoders in combination with position coding, and the attention part of the Transform encoder senses multi-head attention in a dynamic interaction mode; and training an FTC model by using the preprocessed micro-service KPI data, and carrying out micro-service anomaly detection by using the FTC model. The defect that interactivity among indexes is neglected in the prior art is overcome, sensitivity and detection accuracy of abnormal modes are enhanced, and the method is suitable for large-scale micro-service scenes with high real-time requirements.

To provide a video encoding / decoding method for performing intra-prediction on the basis of availability of a reference pixel so as to improve encoding performance.SOLUTION: An intra-prediction method for a color copy mode checks a reference pixel region designated to obtain correlation information, determines a reference pixel processing setting based on a determination of availability of the reference pixel region, and performs intra-prediction according to the determined reference pixel processing. In addition, a candidate list for motion information prediction of the current block is generated, a control point vector of the current block is derived based on the candidate list and a candidate index, a motion vector of the current block is derived based on the control point vector of the current block, and inter prediction is performed on the current block.SELECTED DRAWING: Figure 14

The invention discloses an ENSO prediction method, system and device based on a space-time fusion neural network, and belongs to the field of deep learning model prediction. The invention designs a time-space fusion Transform network, the network designs a parallel encoder-decoder structure, and the time-space information can be effectively extracted from the ocean surface temperature data and the # imgabs0 # index at the same time, so that the prediction precision of the # imgabs1 # index is improved. Meanwhile, the network extracts global features by using an attention mechanism in an encoder of the network, and establishes a remote dependency relationship on a target. Compared with a comparison method, the method has better prediction capability in multiple prediction advance periods, the problem that an existing ENSO prediction method underestimates the ENSO event amplitude along with the increase of the prediction advance periods is solved, and the ENSO prediction precision is improved.

The invention discloses a global ionosphere TEC prediction method based on a coding and decoding space-time Transform model, and belongs to the technical field of ionosphere total electron content prediction, and the method comprises the steps: S1, obtaining and preprocessing TEC, Dst, Kp, SSN and F10.7 data, making a sample, and dividing the data into a training set, a verification set and a test set; s2, a space-time Transform model based on encoding and decoding is constructed, an encoder is composed of a multi-branch convolution layer and a space-time Transform layer, and a decoder is composed of a convolution decoding layer; and S3, training the model by using a training set, optimizing parameters through a loss function, adjusting and optimizing by using a verification set, and evaluating performance and generalization ability by using a test set. According to the method, the global TEC prediction precision is remarkably improved, relatively high prediction performance and robustness can be kept under a severe space weather condition, and efficient extraction of space-time local features and global features is realized.

The invention relates to the technical field of medical image segmentation, in particular to a high-efficiency deep supervised distillation method and device applied to medical image segmentation, and the device comprises a pre-training coding module, a high-efficiency multi-scale fusion block, a multi-scale grouping attention gate, a high-efficiency frequency domain boundary extraction block, a high-efficiency fusion up-sampling block, and knowledge alignment multi-scale distillation. According to the invention, while the multi-scale features are effectively captured and fused, the feature expression capability in jump connection is enhanced; besides, the boundary prediction capability of the model is improved by using frequency domain information, and the multi-scale knowledge of the teacher model and the student model is gradually aligned in the encoding-decoding stage, so that the lightweight student model can still keep excellent segmentation performance under the condition of greatly reducing the calculation cost, and the method has extremely high compatibility, and is suitable for popularization and application. The method can be adaptive to multi-layer visual encoders of different scales, can be adaptive to output of different layers of the encoders, and effectively reduces the calculation amount and the parameter amount while guaranteeing the high segmentation performance.

The invention relates to a graph-based unsupervised fraud detection method. The method comprises the following steps: acquiring original graph data; community division is carried out on nodes of the topological structure of the preprocessed graph data; generating a super node graph; a reconstructor network of a coding-decoding structure is trained, and a reconstructor model is obtained through iterative training; calculating a reconstruction score of the super node; sampling a node 1-ego network; neighbor nodes in the 1-ego network are divided into a same-proton graph and a different-proton graph; training a graph neural network; iteratively training to obtain a graph neural network model; obtaining an isoproton diagram code and an isoproton diagram code; generating a second abnormal score of the target node; and marking the nodes with the comprehensive abnormal scores exceeding an abnormal threshold value as fraud nodes. Compared with the prior art, the method has the advantages of detecting aggregation type and sparse type fraud at the same time and the like.

Provided are a feature encoding / decoding method and apparatus, and a computer-readable recording medium generated by the feature encoding method. The feature decoding method may comprise obtaining first information on a maximum number of internal layers allowed in a coded feature sequence (CFS) from a bitstream, obtaining second information on an identifier for each internal layer and third information on the number of channel layers of the internal layer from the bitstream, based on the first information, obtaining fourth information about an identifier for each channel layer from the bitstream based on the third information, and reconstructing the channel layer based on the fourth information.

The invention discloses a video acquisition method and device based on modular low coupling, and relates to the technical field of video processing. The method comprises the following steps: initializing a video processing system, creating a unified system resource management interface, and realizing centralized management of video acquisition, encoding, decoding, OSD control and NPU push modules; based on a modular design principle, video acquisition, coding, decoding, OSD control and NPU push functions are packaged into independent modules, and data interaction between the modules is realized through a standardized interface. The video processing system is modularly designed, and a unified standardized interface is provided, so that the effect of greatly reducing project code coupling is achieved, a developer does not need to pay attention to underlying logic when calling the interface, and module function independence is guaranteed; by means of the modular interface design, developers can rapidly integrate platform functions, development work can be completed without deeply knowing the whole code library, and therefore the project development efficiency is improved.

The invention discloses a sea-land island remote sensing image segmentation method based on feature difference enhancement fusion, and the method comprises the steps: collecting sea-land island remote sensing image data, and constructing a sea-land island remote sensing image data set; constructing an initial sea-land island remote sensing image segmentation model; calculating a loss function value of the model, performing back propagation, and training to obtain a sea-land island remote sensing image segmentation model; and outputting a segmentation result graph based on the trained model. According to the method, a coding-decoding architecture is adopted, a difference attention fusion module is introduced in a coding stage, and the two features are fused based on complementarity of global and local features so as to enhance the expression ability of the model; a cross fusion module is introduced in the decoding stage, multi-scale feature fusion is performed on features from different coding stages, and the segmentation precision is improved; and finally, outputting a segmentation prediction result through a full connection layer, and realizing accurate training optimization in combination with a multi-term auxiliary loss function, thereby improving the efficiency and accuracy of sea-land island remote sensing image segmentation.

An image encoding / decoding method and apparatus are provided. An image decoding method according to the present disclosure is performed by an image decoding apparatus. The image decoding method comprises constructing a merge candidate list for a current block based on a prediction mode of the current block, deriving motion information of the current block based on the merge candidate list, and generating a prediction block of the current block based on the motion information. Information on a maximum number of merge candidates included in the merge candidate list may be obtained through a sequence parameter set, and, based on the prediction mode being a subblock-based merge mode, the maximum number of merge candidates may be determined based on whether an affine mode is available for the current block.