A system for selecting an image to accompany text from a user in connection with a social media post. The system is capable of receiving text from the user, identifying one or more search terms based on the text, identifying candidate images from images in one or more image databases using the search terms, presenting one or more candidate images to the user, receiving from the user a selected image from the one or more candidate images, generating the social media post comprising the selected image and the user-submitted text, and transmitting the social media post for display.

A system for selecting an image to accompany text from a user in connection with a social media post. The system is capable of receiving text from the user, identifying one or more search terms based on the text, identifying candidate images from images in one or more image databases using the search terms, presenting one or more candidate images to the user, receiving from the user a selected image from the one or more candidate images, generating the social media post comprising the selected image and the user-submitted text, and transmitting the social media post for display.

An image retrieval method, an image retrieval device, and an image retrieval system, which are used for image retrieval, are provided. The image retrieval method comprises: receiving a first original image (S100); extracting an image feature of the first original image to obtain a first feature code (S200); obtaining first target information according to the first feature code (S300); according to the first target information, searching for a first target painting set corresponding to the first target information in at least one of a painting library and a knowledge graph library, so as to obtain the first target painting set (S400); and outputting the first target painting set (S500).

An image retrieval method, an image retrieval device, and an image retrieval system, which are used for image retrieval, are provided. The image retrieval method comprises: receiving a first original image (S100); extracting an image feature of the first original image to obtain a first feature code (S200); obtaining first target information according to the first feature code (S300); according to the first target information, searching for a first target painting set corresponding to the first target information in at least one of a painting library and a knowledge graph library, so as to obtain the first target painting set (S400); and outputting the first target painting set (S500).

A content recommendation method that includes: acquiring content cover images corresponding to multiple pieces of content accessed by a user account; acquiring cover image features of the multiple content cover images, and determining user account features of the user account according to the cover image features of the multiple content cover images; on the basis of cover image features of content to be recommended and the user account features, determining an access probability value of the user account accessing the content to be recommended; and providing, according to the access probability value, the content to be recommended to the user account.

A method for multimodal content retrieval, may include: receiving a search query corresponding to a request for content; aggregating word features extracted from the search query based on a first set of learned weights; aggregating region features extracted from each of a plurality of images, based on a second set of learned weights, independently of the word features; computing a similarity score between the aggregated words features and the aggregated region features for each of the plurality of images; selecting candidate images from the plurality of images based on the similarity scores between each of the plurality of images and the search query; and selecting at least one final image from the candidate images as a response to the search query, based on attended similarity scores of the candidate images with respect to the search query.

A method for multimodal content retrieval, may include: receiving a search query corresponding to a request for content; aggregating word features extracted from the search query based on a first set of learned weights; aggregating region features extracted from each of a plurality of images, based on a second set of learned weights, independently of the word features; computing a similarity score between the aggregated words features and the aggregated region features for each of the plurality of images; selecting candidate images from the plurality of images based on the similarity scores between each of the plurality of images and the search query; and selecting at least one final image from the candidate images as a response to the search query, based on attended similarity scores of the candidate images with respect to the search query.

A contextual query system is configured to perform operations that include: causing display of a graphical user interface at a client device, the graphical user interface including a display of image data that comprises a set of image features; generating a query based on the set of image features of the image data; accessing media content based on the query at a repository, the repository comprising a collection of media content; and causing display of a presentation of the media content within the graphical user interface at the client device.

A contextual query system is configured to perform operations that include: causing display of a graphical user interface at a client device, the graphical user interface including a display of image data that comprises a set of image features; generating a query based on the set of image features of the image data; accessing media content based on the query at a repository, the repository comprising a collection of media content; and causing display of a presentation of the media content within the graphical user interface at the client device.

A service server includes a product database configured to extract a label describing product details, map the label to a product, and store the label; a query processing unit configured to receive a query to request recommended product information related to a search target object from a user device, recognize the search target object from the received query, and obtain a query label from the recognized search target object; a feature label list providing unit configured to search the product database for one or more candidate recommended products, generate a feature label list based on feature labels selected among feature labels tagged on the one or more candidate recommended products, and provide the feature label list to the user device; and a product recommendation module configured to search the product database for a recommended product, and provide the recommended product information to the user device.