21 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 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 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 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.

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.

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 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 embodiment of the invention provides an object category identification method and device based on a multi-modal large model, and a display method and device, and relates to the technical field of artificial intelligence, and the object category identification method based on the multi-modal large model comprises the steps: obtaining a first feature image and first text information of a first object; the first feature image comprises an object attribute image and an environment image of the first object; analyzing the first feature image and the first text information through a pre-trained multi-modal large model to obtain a first object category prediction result of the first object; identifying a target element in the first feature image to obtain a first identification result of the target element; and determining a target object category of the first object according to the first object category prediction result and the first recognition result. The object category identification accuracy can be improved, visual evidence is provided for the object category identification result, and the credibility of the object category identification result is improved.

The invention provides a resource recommendation method and device, equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical field of deep learning, large model and natural language processing, and can be used for application scenes such as generative search, document intelligent editing, intelligent assistants, virtual assistants and intelligent e-commerce. According to the specific implementation scheme, features of a target resource and features of an author of the target resource are input into a first multi-task learning model, and a first prediction result is obtained; inputting the features of the target object into a second multi-task learning model to obtain a second prediction result; wherein the features of the target object comprise interaction features of the target object and the first recommendation stream and interaction features of the target object and an author page of the author; obtaining a third prediction result of the plurality of value factors based on the first prediction result and the second prediction result; and determining whether to add the target resource to a second recommendation stream set for the target object based on a third prediction result of the plurality of value factors.

The invention provides a resource recommendation method and device, a model training method and device, equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of big data, deep learning, intelligent recommendation and the like. The method comprises the steps of selecting a target resource in response to a target object, and obtaining a recommendation reference feature for the target object; according to the recommendation reference features, related resource recommendation parameters for the target object are determined, and the related resource recommendation parameters represent the proportion of recommendation resources, of which the correlation with the target resources meets a preset condition, in a resource recommendation result for the target object; and according to the recommendation reference features and an updated recommendation strategy obtained after a preset recommendation strategy is updated by the related resource recommendation parameters, executing resource recommendation operation to obtain a resource recommendation result for the target object.

The invention provides an object recommendation method, a training method of an object recommendation model and electronic equipment, and relates to the field of artificial intelligence technology and large models. The method comprises the steps that preference information of a user and object information corresponding to a plurality of to-be-recommended objects are obtained, and the preference information is used for representing the preference of the user for the to-be-recommended objects; a plurality of reasoning tasks are executed on the preference information and object information corresponding to the to-be-recommended object through the object recommendation model, a reasoning track corresponding to the to-be-recommended object is generated, and the reasoning track comprises execution results corresponding to the reasoning tasks respectively; determining a target object from the plurality of to-be-recommended objects based on the reasoning trajectories corresponding to the plurality of to-be-recommended objects; and pushing the target object to the user. The technical problem that the model recommendation effect is poor in the prior art is solved.