35 results about "Goal object" patented technology

In an embodiment, a method for human motion generation with open vocabulary scene-and-text context is provided. The method involves receiving an input that includes a 3D point cloud of a scene containing a goal object with a natural language instruction related to the goal object. A text tokenizer is applied to the text to obtain tokenized text, and a text encoder from a pre-trained vision-language model generates text features. First scene features are generated by applying a pre-trained U-Net scene encoder to the 3D point cloud, which are down sampled to obtain second scene features. A conditional latent is obtained by fusing the second scene features with the text features. A conditional motion generator predicts motion parameters for a parametric human body model over a specific time duration. Finally, 3D human meshes for multiple motion frames are obtained based on the motion parameters and the parametric human body model.

The invention provides a task execution method, device and system based on a model context protocol, and relates to the technical field of artificial intelligence, in particular to the technical fields of cloud services, large models, model context protocol services and the like. The task execution method based on the model context protocol comprises the following steps: in response to a received task request related to a target object, determining tool related information associated with an object attribute of the target object in a model context protocol service; tool related information is sent to a model end where the large model is deployed, and target related information determined from the tool related information based on the large model is received; and based on the target related information, calling a target tool for the model context protocol service to execute a target task related to the task request, and obtaining a task execution result.

The invention provides a text-to-map method, device, system and equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, large models and the like, and is applied to scenes such as AIGC content generation based on artificial intelligence. According to the specific implementation scheme, a target dialogue text used for generating an image of a current round of dialogue of a target object is obtained; constructing to-be-rewritten information based on the target dialogue text and the historical dialogue condition; inputting the to-be-rewritten information into the large language model to obtain a target cue word generated by the large language model according to the requirement of the target dialogue text; the target cue word is obtained based on the text rewriting capability of the large language model; and generating an image of the current round of dialogue based on the target prompt word.

The invention relates to the technical field of artificial intelligence, in particular to a content recommendation method and device, electronic equipment and a storage medium, and aims to improve the content recommendation accuracy. The method comprises the following steps: on the basis of the correlation between each browsing duration feature of a target object and a historical behavior feature, respectively weighting historical recommendation content corresponding to each browsing duration feature and a feature element corresponding to the historical behavior feature to obtain a corresponding object interest feature; performing feature fusion based on the object attribute feature of the target object, the content attribute feature of the to-be-recommended content and the interest feature of each object to obtain a target fusion feature; and pushing the to-be-recommended content to the target object when an estimated value obtained based on the target fusion feature meets a preset recommendation condition. Due to the fact that the interest preferences of different levels of the target object can be represented through the obtained interest features of all the objects, the prediction value of the to-be-recommended content is more accurate, and the content recommendation accuracy is improved.

The invention provides an object regulation and control method and device, and relates to the technical field of artificial intelligence. The method comprises the steps that a strategy group is acquired, the strategy group comprises target object information and at least one strategy, and the strategy comprises regulation and control conditions and regulation and control actions which correspond to each other; generating a query task according to the target object information and the regulation and control condition, and executing the query task to obtain regulation and control data; and when the regulation and control data meets a regulation and control condition, executing a regulation and control action corresponding to the regulation and control condition on a target object corresponding to the target object information. Thus, after the user configures the strategy group, the target object can be intelligently regulated and controlled, subsequent human participation is not needed, the regulation and control efficiency of the target object can be effectively improved, and the regulation and control accuracy is improved.

The present disclosure provides a target detection method and a target detection model training method and device, relates to the technical field of artificial intelligence, in particular to the technical field of augmented reality, virtual reality, computer vision, deep learning and the like, and can be applied to scenarios such as smart cities. The implementation scheme is as follows: image data including a target object is obtained, and a target category feature corresponding to a target category of the target object is obtained, wherein the target category feature is fed to a decoder of a target detection model used for target detection; the target category of the target object is predicted based on the target category feature, to obtain a predicted category and a predicted category feature corresponding to the predicted category; the consistency of the predicted category and the target category is determined based on the similarity between the predicted category feature and the target category feature; and the predicted category is taken as a result of target detection on the target object according to the consistency of the predicted category and the target category.

In some examples, systems and methods for object pairings are provided. For example, a method includes: receiving an input associated with at least one of the one or more first values of one or more weights, the one or more weights corresponding to one or more model parameters associated with a task; determining one or more second values of the one or more weights, at least one second value of the one or more second values of the one or more weights being determined based at least in part on the input; modifying the machine-learning model based on the one or more second values of the one or more weights; determining a plurality of object pairings for the task by applying the modified machine-learning model to data associated with the task, each object pairing of the plurality of object pairings including an asset object and the target object.

The present application relates to an object optimization method, apparatus, computer equipment, storage medium and computer program product. The method comprises: obtaining a target object, determining a target deployment environment of the target object; determining at least one optimization target for the target object based on the object data of the target object and the environmental data of the target deployment environment; for each optimization target, performing multi-path optimization on the target object according to a parameter optimization strategy adapted to the target deployment environment, and obtaining a plurality of candidate optimization objects that meet the optimization target; obtaining the object parameters corresponding to each candidate optimization object, and determining the candidate optimization object whose object parameters meet the object parameter screening conditions as the optimization result of the target object. By adopting the method of the embodiment of the present application, customized optimization based on the target deployment environment can be achieved, thereby improving the optimization efficiency of the target object.

The invention provides a resource recommendation method and device and electronic equipment, and relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, natural language processing, computer vision, large models and the like. According to the specific implementation scheme, the method comprises the steps of determining a first satisfaction degree of candidate resources according to candidate resource features of the candidate resources, target object features of a target recommendation object and a sorting model; determining a second satisfaction degree of the candidate resources according to the candidate resource features, the target object features, historical resource features of historical satisfaction resources of the target recommendation object and a large language model; according to the first satisfaction degree and the second satisfaction degree, selecting a to-be-recommended resource from the candidate resources for resource recommendation processing; wherein the second satisfaction is determined in combination with the large language model and the historical resource features, and resource recommendation processing is performed in combination with the first satisfaction and the second satisfaction, so that personalized resource recommendation services can be provided, and the resource recommendation efficiency is improved.

An embodiment of the present application discloses a training method, device, equipment and storage medium for an object selection model, belonging to the field of artificial intelligence technology. The method includes: determining a sample object, where the sample object is set with a target label and n associated labels, and there is a correlation between the target selection task corresponding to the target label and the associated selection tasks corresponding to the associated labels; inputting the sample object information of the sample object into an MMOE model to obtain a first prediction result and n second prediction results output by the MMOE model; training the MMOE model based on the target label, n associated labels, the first prediction result, and n second prediction results; and generating a target object selection model corresponding to the target selection task based on the trained MMOE model.

The application relates to an attribute classification method and device, computer equipment, a storage medium and a computer program product. The attribute classification method comprises the following steps: obtaining historical data of a target object; calculating a first attribute value corresponding to the target object according to a preset interactive object label and historical information; calculating a second attribute value corresponding to the target object according to a referral identity label carried by the target object and a pre-stored label list; and determining a target classification attribute corresponding to the target object according to the first attribute value and the second attribute value. Through the arrangement, the attribute division of the target object can be more accurate, and the target classification attribute obtained finally can be more consistent with the actual attribute of the target object.

The present application relates to a method, apparatus, computer equipment, storage medium and computer program product for determining user similarity, and relates to the field of big data technology, and can be used in the field of financial technology or other related fields. The method comprises: determining the first user similarity between a first user and a second user based on a first rating of a target object by a first user and a second rating of a target object by a second user. Determine the time influence factor of the object, the level influence factor of the object and the time influence factor of the user based on the association characteristics of the first user, the association characteristics of the second user and the evaluation characteristics of the target object, and determine the target user similarity between the first user and the second user in the object push scenario. By adopting this method, when determining the similarity between users, the relationship between the evaluation level of the user and the evaluation level of the object can be fully considered, so as to achieve a more comprehensive, objective and accurate effect of determining the similarity between users.

The embodiment of the invention provides an object recommendation method, device and system, computing equipment and a storage medium, and the method comprises the steps: for any to-be-recommended target object, determining candidate users matched with the target object and the number of recommended users corresponding to the target object; on the basis of the matching relationship between the target object and the candidate user, constructing global matching information of a to-be-recommended object and the user, and performing optimization analysis on the global matching information by taking the condition that the target user number allocated for the target object reaches the recommended user number as a constraint condition; and determining a target recommendation object for each user. The method can improve the object recommendation effect.

The invention provides an interaction method, a method and device for training a large model, an intelligent agent and electronic equipment, and relates to the technical field of artificial intelligence, in particular to the technical fields of large models, deep learning, computer vision and the like. The interaction method comprises the steps of determining an initial query feature, an initial key feature and an initial value feature according to demand information input by a target object; compressing query data blocks in the initial query features and key data blocks in the initial key features to obtain query sub-features and key sub-features; fusing the plurality of query sub-features and the plurality of key sub-features based on an attention mechanism to obtain an attention feature; the feature elements are masked according to mask marks corresponding to the data blocks, target features are obtained, and the mask marks represent that the similarity between the multiple pieces of data in the data blocks meets a preset condition; fusing the initial value feature, the attention feature and the target feature to obtain an intermediate output feature; and determining feedback information related to the demand information according to the intermediate output characteristics.

The invention discloses an information generation method and device, equipment, a medium and a program product, and relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, large models and the like. According to the specific implementation scheme, at least two types of label generation models are utilized, key information extraction is conducted on target objects, candidate label sets for the target objects are obtained, and the target objects comprise at least one of a target image, a target video and a target text; performing semantic consistency processing on the candidate label set according to an analysis result obtained by performing semantic consistency analysis on the candidate label set to obtain a target label set; and performing information generation processing on the target label set and the target object by using the multi-modal large model to obtain description information for the target object.

Embodiments of the present invention disclose a method for generating action instructions, a method for training an action decision-making model, and an apparatus. By obtaining first object features of multiple participating objects in a game session, obtaining second object features of a target object among the multiple participating objects, and using the first label and the second label generated by the action decision-making model to represent the action type and the action object respectively, it is possible to comprehensively and reasonably depict the features of the target object and hierarchize the action labels, which can better adapt to scenarios with a large number of participating objects and complex operations executable for virtual characters, thereby improving the accuracy and rationality of the generated action instructions and enhancing the artificial intelligence level of the target object. Additionally, when training the action decision-making model, by obtaining target result information to determine the reward value of the objective function, the effect of reinforcement learning is achieved. The method provided by the embodiments of the present invention can be applied to scenarios such as artificial intelligence, cloud computing, and games.

The invention relates to the field of artificial intelligence, and provides a method for determining a control strategy model, the control strategy model is used for controlling a target tool to execute a target task of moving at least one target object in a plurality of target objects from an initial position to a target position, and the method comprises the following steps: based on a preset model, determining a control strategy model; at least one action round is iteratively executed for the target task, and each action round in the at least one action round comprises the steps that the target tool is initialized into an initial pose, and a pose updating step is iteratively executed based on a preset model; determining a reward score based on the execution progress of the target task in the execution process of the at least one action round; updating model parameters of a preset model based on the reward score, so that the model parameters converge towards the direction of obtaining the expected reward score; and determining a control strategy model based on the parameter convergence preset model. Therefore, the model can be allowed to explore by itself, so that a control strategy model with good performance and generalization is obtained.

The invention discloses an object storage method and device, and the method comprises the steps: obtaining object description information inputted by a user, and the object description information is used for describing the features of a to-be-generated object; based on the object description information, utilizing an artificial intelligence model to generate at least one object; extracting at least one feature word from the object description information by using an artificial intelligence model, wherein the feature word is used for representing content features of the object; a storage request for a target object is detected, the object name of the target object is generated based on the at least one feature word, and the target object belongs to the at least one object; and associatively storing the target object and the object name of the target object.

The present application relates to an object determination method, apparatus, computer device, and storage medium. It relates to the field of artificial intelligence technology, including: obtaining the index prediction values of each object in the first object set on a preset index; selecting the objects in the first object set whose index prediction values meet the index value screening conditions to obtain a second object set; determining the mapping relationship between the preset index and the object features based on the index experimental values and object features of multiple objects in the first object set on the preset index; determining a target object that meets the index requirements of the preset index from the second object set based on the mapping relationship. Using this method can reduce the time cost.

Embodiments of the present application provide a similar object determination method and device, electronic equipment and storage medium, belonging to the field of artificial intelligence. The method comprises: obtaining candidate characteristic values of multiple object attributes for each candidate object respectively; determining data distribution information of the object attributes according to the candidate characteristic values; sorting the multiple object attributes according to the data distribution information to obtain attribute sorting information; obtaining target characteristic values of the multiple object attributes of a target object, expanding the target characteristic values of the object attributes of the target object in turn according to the attribute sorting information, and obtaining at least one target characteristic range of the object attributes; and matching the target characteristic values of the at least one target characteristic range and the target characteristic values not expanded with the candidate characteristic values of the corresponding object attributes, and determining a similar object of the target object from the multiple candidate objects according to a matching result. The embodiments of the present application can determine key information affecting similar object searching, and improve the explainability and accuracy of the determination method.

The invention provides a resource recommendation method, a feature extraction model training method, devices and equipment, and relates to the technical field of artificial intelligence. The resource recommendation method comprises the following steps: in response to a resource recommendation request, obtaining a historical behavior sequence and candidate resources of a target object contained in the resource recommendation request; inputting the historical interaction resources and the candidate resources into a feature extraction model for feature extraction to obtain respective meta-embedding sets of the historical interaction resources and the candidate resources; determining a target fusion strategy corresponding to a cooperative relationship type from preset fusion strategies according to the cooperative relationship type between the historical interaction resources and the candidate resources; performing fusion processing on the plurality of meta-inserts of the historical interaction resources and the plurality of meta-inserts of the candidate resources to obtain historical resource features of the historical interaction resources and candidate resource features of the candidate resources; and according to the similarity between the historical resource features and the candidate resource features, generating a resource recommendation result for the target object.

The embodiments of the present application provide a method, device, electronic device and computer program medium for generating object description data, which relate to the field of artificial intelligence technology. The method in the embodiments of the present application includes: obtaining object feature information of a target object; based on the object feature information, generating multiple object feature vectors of the target object under the object feature dimension respectively; inputting the multiple object feature vectors into a pre-trained machine learning model; obtaining the preference level label of the target object under the target classification attribute output by the pre-trained machine learning model; if the obtained preference level label matches the preset preference level label, then generating object description data of the target object based on the target classification attribute. The technical solution of the embodiments of the present application improves the accuracy of the obtained object description data.

The present application discloses an object classification method, apparatus, computer device, storage medium and program product, relating to the field of artificial intelligence technology. The method includes: obtaining a target hypergraph, which is composed of n target hyperedges, and each target hyperedge is composed of m target objects having an association relationship with the same target entity, where n and m are positive integers; generating a target object weight matrix based on the target hypergraph; extracting features from the target hypergraph based on the target object weight matrix to obtain target object feature representations respectively corresponding to the respective target objects; and performing object category prediction based on the target object feature representations to obtain a target object category corresponding to the target object. The ability to extract feature information of objects in the target hypergraph is improved, which is beneficial to better constructing high-level object feature representations, thereby providing more accurate object feature representations for subsequent object classification prediction and facilitating the accuracy of subsequent classification prediction tasks.

The present disclosure provides a product recommendation method, a product recommendation model training method, an apparatus, and a device, which can be applied to the fields of artificial intelligence technology and financial technology. The method comprises: with the permission of the target object, converting the object information of the target object into a graph vector, wherein the object information includes a plurality of sub-object information, and the graph vector includes a plurality of node vectors, and the node vector is used to represent the object sub-information; when the target object is determined to be a potential object based on the graph vector, a first sub-graph vector that matches the preset product attribute information is determined from the graph vector based on the node identifiers of the plurality of node vectors; the first sub-graph vector is input into the product recommendation model to obtain the object category of the target object and a product recommendation list that matches both the object category and the target object; and the product to be recommended determined from the product recommendation list is recommended to the target object.

Embodiments of the application disclose a method and related device for constructing object features in the field of artificial intelligence, and the method comprises the following steps: obtaining static attribute information and dynamic behavior information of a target object, wherein the dynamic behavior information is behavior information generated by the target object on a target network platform; determining cold coding features of the target object according to the static attribute information; determining hot coding features of the target object according to the static attribute information and the dynamic behavior information; and performing feature fusion processing on the cold coding features and the hot coding features to obtain object features of the target object on the target network platform. The method can ensure that the constructed object features have high accuracy, thereby facilitating accurate recommendation of the object.

The invention provides a resource recommendation method and device, equipment, a medium and a product, and relates to the technical field of artificial intelligence, in particular to the technical fields of intelligent recommendation, deep learning, large models and the like. The resource recommendation method comprises the steps of obtaining object side features of a target object; the object side features comprise behavior features of the target object in a target page; obtaining a resource side feature of each candidate resource in the plurality of candidate resources; the resource side features comprise resource features, item features of a currently played item and entity features of an associated entity; obtaining a target index based on the object side feature and the resource side feature; determining a target resource in the plurality of candidate resources based on the target index; and recommending the target resource to the target object. According to the invention, the resource recommendation accuracy can be improved.

The application discloses an object recommendation method and related device, and relates to the technical field of artificial intelligence. The application can be applied to the field of map Internet of Vehicles. The method comprises the following steps: constructing an object feature vector representing social relations for an object in a target object set; performing clustering processing on the target object set based on social data to obtain an object cluster; extracting a content feature vector from the object description information of a cold-start object, and determining a target object matching the content feature vector from the target object set; performing calculation and processing on the object feature vector of the target object included in each object cluster to generate a group feature vector; performing fusion processing on the content feature vector and the group feature vector to generate a fusion feature vector; and determining an object to be recommended to the cold-start object from the target object set based on the fusion feature vector. The application effectively improves the social matching degree and recommendation diversity of the recommended object and the cold-start object.

The present disclosure provides a resource recommendation method, a model training method, an apparatus, a device and a medium, relates to the technical field of artificial intelligence, and in particular to the technical field of big data, deep learning, intelligent recommendation and the like. The method comprises: in response to a target object selecting a target resource, obtaining a recommendation reference feature for the target object; determining a relevant resource recommendation parameter for the target object according to the recommendation reference feature, wherein the relevant resource recommendation parameter represents a proportion of recommended resources in a resource recommendation result for the target object, which have a relevance to the target resource satisfying a preset condition; and performing a resource recommendation operation according to the recommendation reference feature and an updated recommendation strategy obtained by updating the preset recommendation strategy according to the relevant resource recommendation parameter, to obtain the resource recommendation result for the target object.

The invention relates to a zero-sample target navigation method and system based on object perception memory, and the method comprises the steps: S1, obtaining environment observation information at a current moment, constructing an occupancy graph, and extracting a leading edge point; s2, caching the environment observation information to a memory buffer area; s3, screening related historical observation frames of each leading edge point from the memory buffer area according to the visibility constraint and the distance constraint; s4, extracting an object region image sequence based on the related historical observation frame; s5, in combination with the natural language description of the target object and the image sequence, obtaining a semantic matching score of each leading edge point, and selecting a target exploration point based on the semantic matching scores; and S6, performing path planning and navigation control in the occupancy map according to the target exploration point, terminating the navigation task when the target object is identified in the visual field, and otherwise, returning to S1 until navigation succeeds or the preset step number upper limit is reached. According to the invention, the accuracy of target positioning and the efficiency of the navigation path can be improved.

The invention provides a data processing method and device based on artificial intelligence, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of generating an object relation graph structure based on relation data; performing first embedding processing based on a graph structure similarity mechanism on the object relation graph structure to obtain a first relation feature of the candidate object, and performing second embedding processing based on a random walk mechanism on the object relation graph structure to obtain a second relation feature of the candidate object; based on the first relation feature of the candidate object, the second relation feature of the candidate object and the object feature of the candidate object, generating a team forming probability of the candidate object; and determining a candidate object to be invited of the target object based on the team forming probability of the candidate object, and executing an invitation operation of the corresponding target object for the candidate object to be invited. Through the application, the success rate of inviting the target object to form the team can be improved, so that the man-machine interaction efficiency is improved.