12 results about "Goal object" patented technology

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.

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.

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.

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 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 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 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.