92 results about "Text enhancement" patented technology

The invention belongs to the technical field of network security, and particularly discloses a network security knowledge graph construction and attack path prediction method, which comprises the following steps: acquiring an attack mode of network threat intelligence; constructing a network security knowledge graph based on the security vulnerability and attack pattern classification standard data and the attack pattern of the network threat intelligence; according to the method, an entity relationship in a network security knowledge graph is predicted based on a graph attention network GAT of text enhancement, an attack path is constructed based on the predicted entity relationship, and text enhancement is to introduce text information corresponding to entity nodes into a multi-head attention mechanism layer of the GAT. According to the method, the network security knowledge graph is constructed and the entity relationships are predicted based on the GAT, so that the entity relationships can be quickly integrated, the attack paths are constructed, the paths reveal security holes and attack modes which may be utilized by attackers, and accurate and efficient attack path prediction can be realized.

The invention relates to a DVS vibration signal semantic representation method based on prior knowledge enhancement, and relates to the field of signal processing and artificial intelligence. The method comprises the following steps: (1) constructing a distributed vibration signal text enhancement data set; (2) cross-diffusing the standardized DVS vibration signal; (3) multi-band input alignment is carried out; (4) residual signal enhancement; (5) DVS vibration signal feature extraction; (6) aligning and mapping DVS vibration signal semantic features; and (7) fine adjustment of the DVSLLM model. According to the method, the technical problems that in the field of signal processing, sampling frequencies are not uniform, spatiotemporal features are difficult to extract, semantic information is difficult to represent and the like are solved, and a text enhancement data set is constructed by utilizing Deepseek-V3; a fine-grained interaction model from DVS vibration signals to natural language questions and answers is constructed through cross diffusion standardization, two-dimensional discrete transformation, multi-scale convolution, a CBAM attention mechanism, bilingual sense mapping alignment and DVSLLM model fine tuning, and an intelligent method is provided for road roadbed vibration event health monitoring.

The invention discloses a medical report generation method, a model training method, equipment and a medium, and the model training method comprises the steps: constructing a medical report generation model framework which comprises a global semantic collaborative multi-modal enhancement module, a visual encoder, a text encoder, a medical insight analyzer and an LLM decoder; wherein the global semantic collaborative multi-modal enhancement module respectively enhances a medical image and a medical report by utilizing a selected image enhancement strategy and a text enhancement strategy, and the medical insight analyzer comprises a fine-grained structure learning device and a global context guide learning device which are connected in sequence so as to enhance the cross-modal alignment capability; and performing intelligent collaborative optimization by taking a strategy set formed by an image enhancement strategy and a text enhancement strategy and architecture configuration parameters of the medical insight analyzer as optimization targets to obtain an optimal medical report generation model. The medical report generation performance can be effectively improved.

The invention discloses a facial action unit recognition method and system based on multi-modal fusion and text enhancement. The method comprises the following steps: extracting a facial image attribute set by a visual language model, constructing an adaptive text cue word based on the facial image attribute set, and processing the adaptive text cue word by adopting CLIP to obtain a text semantic feature; extracting facial key point features by the facial key point detection model; the facial expression recognition model extracts facial expression features; the image semantic feature fusion module processes the facial key point features and the facial expression features to obtain multi-modal image semantic features; inputting the multi-modal image semantic features and the text semantic features into a multi-modal feature fusion module to obtain multi-modal fusion features after text information enhancement; the recognition of the facial action unit of the facial image is realized. According to the method, the complementarity of cross-modal features and the robustness of joint representation can be enhanced, and the recognition precision of facial actions in a complex scene is effectively improved.

The invention discloses a multi-relation extraction error propagation optimization method and device based on data collaborative enhancement, and the method comprises the steps: generating extended training data through grammar recombination and adversarial samples, carrying out the positioning and weighted sampling of a low-frequency relation according to an entity pair relative position, and obtaining an upper relation data set; and training an upper relation classifier based on the upper relation data set, and performing prediction through the trained upper relation classifier to obtain a prediction result and confidence distribution thereof. According to the method, through text enhancement and a weighted sampling strategy for relative position positioning based on entities, the problem of unbalanced data distribution is effectively relieved, the modeling capability of the upper relation classifier for long-tail distribution is remarkably improved, and therefore systematic optimization of the upper relation classifier for low-frequency relation recognition accuracy is achieved.

The invention discloses a multi-modal face living body detection method and device based on text enhancement, and the method comprises the steps: inputting a multi-modal face image into an image block embedding module and a visual prompt generation module, and carrying out the encoding through an image encoder, thereby obtaining an image feature and a visual prompt; inputting the dichotomy text description into a text embedding module, randomly initializing a text prompt vector, and encoding through a text encoder to obtain text features and text prompts; respectively enhancing image features and visual prompts by using a hybrid expert module and a bypass prompt enhancement module; performing information exchange on the visual prompt and the text prompt by using a text enhancement module to obtain an enhanced text prompt, and performing information exchange on the image feature and the text feature by using an image mask module to obtain an enhanced image feature; and finally calculating the similarity between the image features and the text features, and taking the category corresponding to the highest similarity as a detection result. According to the invention, the method can effectively enhance the discrimination capability of human face features, and improves the detection accuracy and generalization.

The invention relates to a multi-modal sentiment analysis method based on text enhancement and modal completion perception fusion, and belongs to the field of natural language processing. The method comprises the following steps: 1, extracting text semantic features by utilizing a pre-training language model, and performing linear transformation on non-text features to form unified multi-modal input representation; 2, injecting a cross-modal enhancement module into the pre-training language model, fusing non-text information by taking a text as a core, and performing multi-modal input representation; 3, introducing a modal completion module, generating a missing modal completion representation through reconstruction loss and random modal discarding, and suppressing noise features in combination with a convolution gating structure; and guiding full-connection network learning modal weight to realize joint optimization of sentiment classification and regression. According to the method provided by the invention, on the premise of keeping the dominance of the text, through cross-modal interaction and dynamic weighting and in combination with a modal completion mechanism, the model has relatively strong emotion recognition capability.

The invention belongs to the technical field of artificial intelligence, and relates to an LSH-based retrieval enhancement generation method and device, equipment and a storage medium thereof. Carrying out serialization processing; sampling processing is carried out through the vector space model, and high-dimensional embedded vector representation is generated; inputting the high-dimensional embedded vector representation into a vector database, and carrying out matching calculation; carrying out binary code valuation conversion on a matching calculation result by adopting an LSH algorithm to obtain binary code values corresponding to all the retrieved texts after conversion; according to the binary code value, an expected retrieved text is screened out; combining and sorting the retrieval input text and the expected retrieved text to generate a to-be-enhanced text; and inputting the text to be enhanced into the text enhancement generator to generate an enhanced retrieval feedback text. The method is applied to webpage search, question and answer systems and recommendation system scenes in the field of financial services or medical services, and a faster and more accurate retrieval feedback result is provided for retrieval personnel.

The invention relates to the field of household appliances, and provides a smart home multi-modal dialogue method, device and equipment and a medium, and the method comprises the steps: inputting equipment dialogue data into a multi-modal dialogue model, and extracting word features of text information in the equipment dialogue data, part-of-speech features corresponding to the word features and relation features between adjacent word features, performing text enhancement on the word features and the part-of-speech features according to the relation features, extracting context vectors according to text information, combining the part-of-speech features and the word features after text enhancement to obtain a decoded word sequence, predicting a user emotional state according to device dialogue data, and combining the decoded word sequence to generate a reply prediction result. According to the method, the defect that a multi-mode dialogue technology is difficult to meet human emotion requirements is overcome, and it can be ensured that the generated reply not only conforms to logic in content, but also is matched with a user state in emotion expression.

The invention relates to a long-tail recommendation method and system based on multi-agent collaborative reasoning, and belongs to the technical field of personalized recommendation. The method comprises the following steps: performing text enhancement on user and project data, and constructing a semantic data set; constructing and training a candidate item retrieval model with long-tail perception capability; searching candidate items for a target user, and constructing a deep user portrait containing a personalized novelty exploration coefficient; starting a multi-agent collaborative reasoning process, and rearranging candidate items through multi-dimensional evaluation, strategy sorting and adaptive causal depolarization; and finally, generating a personalized recommendation list, and performing iterative optimization based on user feedback. According to the method, the recommendation task is decomposed into a collaborative reasoning step of a plurality of agents, and a personalized and causal-driven prejudice removing mechanism is introduced, so that the accuracy of overall recommendation is not damaged while the exposure and recommendation diversity of the long-tail project are improved, and a more fair and transparent recommendation service conforming to the real exploration intention of the user is provided for the user.

The invention provides a long-tail commodity recommendation method based on text enhancement and popularity smoothing. The long-tail commodity recommendation method comprises the steps of obtaining user-commodity interaction data and commodity text data; converting the commodity information into text embedding by using a pre-trained large language model; constructing a user-commodity bipartite graph based on the interaction data, and executing graph convolution to obtain graph embedding of the user and the commodity; performing spatial transformation on text embedding of the commodity, and fusing graph embedding of the commodity and text embedding after transformation; and calculating preference scores of the users on the commodities by using graph embedding of the users and fusion embedding of the commodities, sorting the preference scores from high to low, and recommending the first k commodities to the users. According to the method, text information is effectively fused into commodity embedding through two dimensions of text enhancement and popularity smoothing, and an implicit semantic relation between hot and long-tail commodities is constructed; in the model training process, the influence of various interactions is balanced, the opportunities of small people and new commodities participating in training are increased, and the long-tail problem is effectively relieved by adjusting the exposure rates of different commodities.

The invention provides a text video retrieval method of a fine-grained relation learning network based on energy perception. The method comprises the following steps: giving a query text and a video clip; inputting the query text into a text encoder of the CLIP, inputting the video clip into a text encoder image encoder of the CLIP, and extracting to obtain text embedding and frame embedding; inputting the text embedding and the frame embedding into a fine-grained relation learning network for text enhancement operation to obtain enhanced text embedding; taking the enhanced text embedding as a frame fusion condition to carry out frame fusion operation on the frame embedding to obtain video embedding; calculating the similarity of a text-video pair formed by the query text and the video embedding based on a cosine similarity function; and selecting the text-video pair with the highest similarity as a retrieval output result of text video retrieval. According to the method, the problem of randomness of the random text of single sampling is solved, so that semantic information of text coding is better expanded, and the final retrieval effect is improved.

The invention discloses a multi-modal sentiment analysis method and device based on mutual information and cross-modal text enhancement, and the method comprises the steps: (1) carrying out the preprocessing of a public data set, and respectively extracting text, visual and acoustic features in the data set; (2) constructing an MICTE multi-modal sentiment analysis model, effectively fusing rich sentiment information in text modal information into a visual modal and an acoustic modal through a cross-modal text attention enhancement module CTEA, enhancing the relevance between modals at an input level through a mutual information maximization module MIM, further extracting key information related to tasks at a fusion level, and obtaining an MICTE multi-modal sentiment analysis model; eliminating redundant information; (3) training and verifying the MICTE model by using the training set and the verification set, and selecting the model with the best performance as a final model; and (4) inputting the test set into the finally trained MICTE model to obtain an emotion analysis result. According to the multi-modal sentiment analysis method, the relevance among different modals is deeply mined, and the quality of the fused modals is remarkably improved, so that the accuracy of multi-modal sentiment analysis is improved.

The application discloses a cross-domain face anti-counterfeiting detection method and device based on multi-modal text enhancement, and relates to the technical field of network information security.The method comprises the following steps: inputting two types of description texts into a pre-trained text encoder to extract text category features representing authenticity / fraud, and inputting an image into a pre-trained visual encoder to extract visual features; adding trainable text prompts to each layer of the text encoder, and adding trainable visual prompts to each layer of the visual encoder, wherein the visual prompt of each layer of the visual encoder is obtained by converting the text prompt of the current layer through a full connection layer; embedding a PFT module and a TIM module into the middle layer of each layer of the text encoder and the visual encoder to realize feature interaction and fusion, obtain the cosine similarity and the mask between the text category features and the visual features, and perform face true / false classification.The application simultaneously completes the modal feature interaction in the feature extraction process based on the PFT module and the TIM module, and improves the cross-domain detection performance.

The invention relates to an intelligent restoration method for handwriting archive fonts, and the method comprises the steps: decomposing a handwriting archive target image, obtaining channel images corresponding to all color channels, for each channel image, determining a character enhancement coefficient of each pixel point in a character region based on the image feature value of each pixel point in the character region of the channel image, and restoring the character enhancement coefficient of each pixel point in the character region. And then, based on each character enhancement coefficient, correcting a preset Gaussian convolution scale parameter to obtain a target Gaussian convolution scale parameter corresponding to each pixel point in each channel image, and based on a Retinex algorithm and each target Gaussian convolution scale parameter, carrying out character enhancement processing on the corresponding channel image. According to the method, channel images subjected to character enhancement processing are obtained, the channel images subjected to character enhancement processing are combined, the character enhancement image after the target image of the handwritten file is restored is obtained, and restoration of the target image of the handwritten file is achieved.

The invention relates to an input disturbance robustness improving method for a power generation type large language model, which comprises the following steps of: randomly extracting corpus data from an initial corpus to carry out first disturbance enhancement, and generating a disturbance enhancement corpus; corpora are randomly extracted from the new corpus, text enhancement is carried out in a synonym replacement and / or random character enhancement mode, and text enhancement corpora are generated; combining the initial corpus, the disturbance enhancement corpus and the text enhancement corpus to generate a training corpus; inputting data in the training corpus as a training sample into the generative large language model, randomly discarding elements of model neurons by an input layer and a hidden layer of the model, and setting the elements to zero; and the consistency loss of the generative large language model is optimized. The method has the advantages that the disturbance robustness training method based on disturbance enhancement, text enhancement and feature generalization is adopted, the anti-input disturbance capacity is enhanced, and the generalization capacity and robustness of the power generation type large language model are effectively improved.

The invention discloses an information generation method based on a large language model, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining user input data and a task processing type; according to the task processing type, the multi-modal large model is used for processing user input data, the multi-modal large model comprises a data fusion module, an encoder module, an enhancement module, an image-text description module and a result generation module, and the data fusion module is used for receiving the user input data and fusing the user input data into text data; the encoder module is used for encoding the data output by the data fusion module, the enhancement module is used for performing text enhancement on the data output by the data fusion module according to external knowledge, and the image-text description module is used for generating image-text description data according to the data output by the encoder module; and the result generation module is used for generating information according to the data output by the encoder module, the data output by the enhancement module and the image-text description data, so that the information generation accuracy and content richness of the model can be improved.

The application relates to a human body behavior recognition method and system based on visual semantic text enhancement, and the method comprises the following steps: S10, acquiring human body behavior WIFI sensing data and human body behavior video frame data; S20, respectively extracting WIFI amplitude energy images and high-dimensional semantic feature vectors from the human body behavior WIFI sensing data and the human body behavior video frame data; S30, performing deep correlation fusion on the WIFI amplitude energy images and the high-dimensional semantic feature vectors, and calculating the probability distribution of human body action categories. The human body behavior recognition method based on visual semantic text enhancement provided by the application realizes effective fusion of semantic information and WiFi features by cross-modal similarity retrieval, and calculates the probability distribution of human body action categories based on the global feature vector obtained after fusion, so that the recognition precision of similar actions is effectively improved.

The invention discloses an information generation method and device based on a large language model, a storage medium and a refrigeration equipment system, and belongs to the technical field of artificial intelligence. According to the task processing type, the multi-modal generation type large model is used for processing the user input data, the multi-modal generation type large model comprises a data fusion module, an encoder module, an enhancement module and a result generation module, the data fusion module is used for receiving the user input data and fusing the user input data into text data; the encoder module is used for encoding the data output by the data fusion module, and the enhancement module is used for performing text enhancement according to external knowledge, the data with context features output by the encoder module and the data output by the data fusion module. And the result generation module is used for generating information according to the data output by the encoder module and the data output by the enhancement module, so that the information generation accuracy and content richness of the model can be improved.

According to the verbal skill abstract generation method based on the pointer mechanism, paragraph segmentation and entity labeling are carried out on an original verbal skill problem in advance, and semantic analysis and information extraction can be carried out more accurately in the follow-up process. And then text enhancement is carried out on the preprocessed verbal skill text through a field enhancement model, so that insurance verbal skill technical terms, clause logics and complex relations between the insurance verbal skill technical terms and the clause logics in the verbal skill text can be better understood, and key information, most critical clause contents and logic structures in the verbal skill can be more accurately captured. According to the method, the pointer generation network is combined with the field enhanced text representation, so that the generated abstract can accurately reflect key information and core logic in insurance verbal skills, automatic key point extraction of complex insurance terms is realized, and seat service efficiency and compliance are improved. The method can be applied to insurance recommendation verbal skill scenes in the financial field and the medical field, and seat service efficiency and compliance are improved.

This application proposes a social text enhancement method based on multimodal retrieval and keyword extraction, comprising: S1, extracting keywords from sample sentences of different categories using a category keyword extraction algorithm; S2, training a sentence generation model using an RNN model combined with a self-attention mechanism, and controlling the sentence generation direction of the generation model according to the keywords of the corresponding categories of the training samples; S3, inputting the original sentence into the generation model to generate a first generated sentence with text enhancement; S4, based on a multimodal retrieval algorithm, determining whether the first generated sentence contains keywords from the keyword file; if so, finding the keyword to be replaced in the first generated sentence, and retrieving synonyms of the keyword to be replaced for replacement, thereby generating multiple second generated sentences with data enhancement. This application has the effect of controlling the generation direction and number of generated sentences of the generation model.

The invention relates to the technical field of computers, and discloses an image paper-like processing method and device and electronic equipment. The method comprises the following steps: acquiring two continuous frames of images corresponding to a target interface, wherein the target interface is an interface which contains text content and is about to realize a paper-like effect; performing text screening according to the two continuous frames of images to obtain a text region to be subjected to text enhancement; performing character stroke segmentation processing on the text region to obtain a stroke segmentation result; obtaining a stroke filling value based on the stroke segmentation result; obtaining an initial paper-like picture corresponding to the target interface; and performing enhancement processing on strokes in the initial paper-like picture by adopting the stroke filling value to obtain a final paper-like picture corresponding to the target interface. According to the application, non-native applications, pictures and video contents can achieve a paper-like effect, and the reading experience of an electronic screen is improved.

The invention relates to the technical field of image detection, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses an object position control method, device, equipment and medium based on a text graph model.The method comprises the steps that an initial image and an initial position description text of a target object are obtained, binarization processing is conducted on the initial image, and the initial position description text is obtained; obtaining a target mask image; extracting geometric features of the target object in the target mask image, and determining object position data of the target object according to the geometric features; performing text enhancement processing on the initial position description text according to the object position data to obtain a target description text; and performing semantic coding on the target description text to obtain a semantic feature vector, and generating a corresponding target object image according to the semantic feature vector. The object position generation accuracy and the image generation efficiency can be improved.

The invention discloses a railway signal fault text enhancement method, system and device based on improved EDA, and the method comprises the following steps: constructing a railway field entity dictionary, carrying out the proper noun recognition of a preprocessed to-be-enhanced railway signal fault text, and outputting a structured text; on the basis of a railway signal fault related text data set, a similar word bank of railway signal fault text related vocabularies is generated by adopting a Word2Vec model and used for replacing non-professional vocabularies in railway signal fault text statements, and enhancement operation is performed on the basis of a structured text and the similar word bank of the railway signal fault text related vocabularies by adopting an EDA improvement method. Enhancing the railway signal fault text to be enhanced; and screening the enhanced railway signal fault text. According to the method, the integrity and accuracy of domain terms are ensured through an entity fixing mechanism, an enhanced sample conforming to a real scene is generated in combination with a semantic constraint editing strategy, and the problem of unbalanced data distribution is effectively solved.

This invention presents a robust visual question-answering model training method based on contrastive learning, belonging to the interdisciplinary field of natural language processing and computer vision. In image enhancement, this invention employs a visual context perturbation-based enhancement method. It filters visual contexts with weak relevance to the question by analyzing the attention distribution of objects in the image and adds perturbations to these contexts to construct new image representations, allowing the model to learn visual context-independent image representations. For general question types, a strategy of removing interrogative auxiliary verbs is used for text enhancement; for other question types, a rewriting strategy is employed. Positive samples are constructed using these data enhancement methods, and then optimized using contrastive learning to learn an unbiased multimodal representation of the input information. This invention is applicable to fields such as artificial intelligence and natural language processing, enhancing model robustness and improving the accuracy of question answering on data with different scenarios or distributions.

The invention relates to a text emotional state recognition method based on an MEDTM model, which is used for researching and efficiently utilizing texts in different answers to carry out depression recognition. Firstly, a text feature extraction model is established, various text features are extracted respectively, then different text features are enhanced based on a text enhancement model, emotional state recognition is carried out through an attention fusion module and a self-supervision loss module in an MEDTM model, the problem that text emotion recognition is too short can be effectively solved, and the emotion recognition efficiency is improved. Therefore, the emotion state recognition accuracy is improved.

An emotion support robot man-machine interaction method based on multi-modal emotion recognition belongs to the field of man-machine interaction technology, artificial intelligence and emotion calculation, and comprises the following steps: step 1, carrying out multi-modal emotion recognition on a user; 2, performing personalized emotion support dialogue generation based on an emotion recognition result in the step 1; and 3, transmitting the emotion recognition result in the step 1 and the emotion support dialogue in the step 2 to an interaction robot, determining a corresponding interaction action by the interaction robot according to the emotion recognition result, and outputting a personalized emotion support dialogue to a user through a TTS technology. According to the method, the multi-modal emotion data set for the college student group is constructed, the multi-modal emotion recognition model based on text enhancement is adopted, various complex emotions possibly possessed by the user at the same time can be recognized, quantitative calculation is conducted on the complex emotions, and therefore the current emotion state of the user can be recognized more accurately and more robustly, and the user experience is improved. And the limitation of single-mode identification is overcome.

The invention relates to the technical field of image processing, in particular to a character enhancement processing method and device, electronic equipment and a readable storage medium, and the character enhancement processing method comprises the steps: obtaining a text image from an original grayscale image; performing elimination operation on character rendering in the text image to obtain a text adjustment image; and superposing the text adjustment image to a to-be-displayed image to obtain a text enhancement image. According to the technical scheme, the text part is separated from the original grayscale image to obtain the text image, and the text rendering in the text image is eliminated to obtain the text adjustment image, so that the text display defect caused by a'blooming 'phenomenon can be overcome, and then the text is superposed on the original grayscale image processed by the halftone algorithm; therefore, even if the display effect of the character part processed by the halftone algorithm is poor, the supplement of character defect information is realized due to superposition of the text adjustment image, and the character display effect is improved.

The present invention relates to a visual language navigation method based on dual semantic understanding and fusion, comprising the following steps: obtaining guiding instructions and visual observation images; extracting image features of the visual observation images and item features in the images; extracting guiding instruction long text features based on a natural language encoding network, and extracting guiding keyword text features based on expert experience rules; obtaining item-level image enhancement features and guiding keyword-based text enhancement features to obtain dual semantically enhanced visual and language features, and inputting these features into a visual-language cross-fusion module to obtain cross-modal fusion features; based on the cross-modal fusion features, using an action prediction subnetwork module to output a navigation position prediction result, control the robot to move toward the predicted navigation position, and update the corresponding visual observation image. Compared with existing technologies, the present invention has the advantages of high action prediction accuracy and strong interpretability.

The invention provides an image segmentation model generation method and device, an image segmentation method and device and electronic equipment, and belongs to the technical field of medical image processing and computer vision. The structure of the initial model sequentially comprises an image representation module used for converting a medical image into a first feature vector, a text representation module used for converting a medical text into a second feature vector, a feature fine alignment module used for obtaining a text enhancement feature based on alignment of the first feature vector and the second feature vector, and a feature fine alignment module used for obtaining a text enhancement feature based on alignment of the first feature vector and the second feature vector. The mask segmentation module is used for obtaining a prediction result based on the text enhancement feature and calculating a loss function value of the prediction result; and training the initial model based on the acquired medical image data and medical text data to obtain a completely trained image segmentation model. According to the invention, the technical problem of poor image segmentation performance in the prior art is effectively solved.