Disclosed herein are methods, systems, and devices for facilitating in determining, logging, and analyzing intelligence, surveillance, and reconnaissance (ISR) information in near real-time. According to one embodiment, a method is implemented on a server for facilitating identification tagging and logging of ISR information. The method includes receiving georeferenced map data and receiving image data. The method further includes transmitting the georeferenced map data and image data to a client device. The method also includes receiving ISR data from the client device and storing the ISR data. In another embodiment, a method implemented on a client device for facilitating an analyst in determining and logging ISR information is disclosed. The method includes receiving georeferenced map data, displaying the georeferenced map data on a GUI implemented on the client device, receiving analyst provided input from the GUI, and transmitting ISR data to a server.

Disclosed herein are methods, systems, and devices for facilitating in determining, logging, and analyzing intelligence, surveillance, and reconnaissance (ISR) information in near real-time. According to one embodiment, a method is implemented on a server for facilitating identification tagging and logging of ISR information. The method includes receiving georeferenced map data and receiving image data. The method further includes transmitting the georeferenced map data and image data to a client device. The method also includes receiving ISR data from the client device and storing the ISR data. In another embodiment, a method implemented on a client device for facilitating an analyst in determining and logging ISR information is disclosed. The method includes receiving georeferenced map data, displaying the georeferenced map data on a GUI implemented on the client device, receiving analyst provided input from the GUI, and transmitting ISR data to a server.

Embodiments of the present invention disclose a method and an apparatus of establishing an image search relevance prediction model, and an image search method and apparatus. The method of establishing an image search relevance prediction model comprises: training a pre-constructed original deep neural network by using a training sample, wherein the training sample comprises: a query and image data, and the original deep neural network comprises: a representation vector generation network and a relevance calculation network; and using the trained original deep neural network as the image search relevance prediction model. The technical solution of the present invention optimizes the existing image search technology, and has stronger capabilities than the prior art as well as various integrations and variations in terms of semantic matching between a query and an image text, semantic matching between a query and image content, click generalization and the like.

A method including pre-screening second images from a database for a search result based on a first image and also based on respective substring distances between first binary substrings for the first image and respective second binary substrings for each of the second images. The method further can include after the pre-screening, determining a respective image distance for each third image of third images of the search result, wherein the respective image distance is between the first image and the each third image of the third images of the search result, and after determining the respective image distance for each third image of the third images of the search result, when the respective image distance for a particular one of the third images is greater than a predetermined image distance threshold, removing the particular one of the third images from the search result before the search result is displayed on a user device requesting the search result. Other embodiments are disclosed.

A method including pre-screening second images from a database for a search result based on a first image and also based on respective substring distances between first binary substrings for the first image and respective second binary substrings for each of the second images. The method further can include after the pre-screening, determining a respective image distance for each third image of third images of the search result, wherein the respective image distance is between the first image and the each third image of the third images of the search result, and after determining the respective image distance for each third image of the third images of the search result, when the respective image distance for a particular one of the third images is greater than a predetermined image distance threshold, removing the particular one of the third images from the search result before the search result is displayed on a user device requesting the search result. Other embodiments are disclosed.

A system and method and for retrieving one or more visual assets includes receiving a search query for the one or more visual assets, the search query including textual data, encoding the textual data into one or more text embedding representations via a trained text representation machine-learning (ML) model, transmitting the one or more text embedding representations to a matching and selection unit, providing visual embedding representations of one or more visual assets to the matching and selection unit, comparing, by the matching and selection unit, the one or more text embedding representations to the visual embedding representations to identify one or more visual asset search results, and providing the one or more visual asset search results for display.

A system and method and for retrieving one or more visual assets includes receiving a search query for the one or more visual assets, the search query including textual data, encoding the textual data into one or more text embedding representations via a trained text representation machine-learning (ML) model, transmitting the one or more text embedding representations to a matching and selection unit, providing visual embedding representations of one or more visual assets to the matching and selection unit, comparing, by the matching and selection unit, the one or more text embedding representations to the visual embedding representations to identify one or more visual asset search results, and providing the one or more visual asset search results for display.

Systems, methods, and non-transitory computer-readable media are disclosed for determining a glyph and a font from a vector outline by applying various combinations of hash-based querying, path-descriptor matching, or anchor-point matching. For example, the disclosed systems can select a subset of candidate glyphs for a vector outline based on (i) comparing hash keys of candidate glyphs with a point-order-agnostic hash key corresponding to the vector outline and (ii) comparing a path descriptor for a primary path of the vector outline to path descriptors corresponding to candidate glyphs. By further comparing anchor points between the vector outline and the subset of candidate glyphs, the disclosed systems can select both a glyph and a font matching the vector outline.

Systems, methods, and non-transitory computer-readable media are disclosed for determining a glyph and a font from a vector outline by applying various combinations of hash-based querying, path-descriptor matching, or anchor-point matching. For example, the disclosed systems can select a subset of candidate glyphs for a vector outline based on (i) comparing hash keys of candidate glyphs with a point-order-agnostic hash key corresponding to the vector outline and (ii) comparing a path descriptor for a primary path of the vector outline to path descriptors corresponding to candidate glyphs. By further comparing anchor points between the vector outline and the subset of candidate glyphs, the disclosed systems can select both a glyph and a font matching the vector outline.

A super-resolution method, a super-resolution system, a user equipment and a server. The super-resolution method includes: training an image sample at a server; obtaining a server-side image database; updating a local image database in a user device by using the server-side image database; and displaying an input low-resolution image as a high-resolution image.