Abstract: Systems, methods and non-transitory computer readable media for prompt-based attribution of generated media contents to training examples are provided. In some examples, a first media content generated using a generative model in response to a first textual input may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may include a respective textual content and a respective media content. Properties of the first textual input and properties of the textual contents included in the plurality of training examples may be used to attribute the first media content to a first subgroup of the plurality of training examples. The training examples of the first subgroup may be associated with a source. Further, a data-record associated with the source may be updated based on the attribution.

Abstract: Systems, methods and non-transitory computer readable media for attributing generated visual content to training examples are provided. A first visual content generated using a generative model may be received. The generative model may be associated with a plurality of training examples. Each training example may be associated with a visual content. Properties of the first visual content may be determined. Each visual content associated with a training example may be analyzed to determine properties of the visual content. The properties of the first visual content and the properties of the visual contents associated with the plurality of training examples may be used to attribute the first visual content to a subgroup of the plurality of training examples. The visual contents associated with the training examples of the subgroup may be associated with a source. A data-record associated with the source may be updated based on the attribution.

Abstract: A system for processing images captured in a retail store and automatically identifying products displayed on shelving units, includes at least one processor configured to: receive a first image depicting a shelving unit with a plurality of products of differing product types displayed thereon and a first plurality of labels coupled to the shelving unit; receive a set of second images depicting a second plurality of labels; correlate a first label depiction of a specific label included in the first image to a second label depiction of the same specific label included in a second image; use information derived from the second label depiction to determine a type of products displayed in the first image in proximity to the specific label; and initiate an action based on the determined type of products displayed in proximity to the specific label.

Abstract: Systems, methods and non-transitory computer readable media for identifying prompts used for training of inference models are provided. In some examples, a specific textual prompt in a natural language may be received. Further, data based on at least one parameter of an inference model may be accessed. The inference model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may include a respective textual content and a respective media content. The data and the specific textual prompt may be analyzed to determine a likelihood that the specific textual prompt is included in at least one training example of the plurality of training examples. A digital signal indicative of the likelihood that the specific textual prompt is included in at least one training example of the plurality of training examples may be generated.

Abstract: Systems, methods and non-transitory computer readable media for optimizing visual contents are provided. A particular mathematical object corresponding to a particular visual content in a mathematical space including a plurality of mathematical objects corresponding to visual contents may be determined. The mathematical space and the particular mathematical object may be used to obtain first and second mathematical objects of the plurality of mathematical objects. The visual content corresponding to the first mathematical object may be used in a communication with a first user and the visual content corresponding to the second mathematical object may be used in a communication with a second user. Indications of the reactions of the first and second users to the communications may be received. A third visual content may be obtained based on the reactions. The third visual content may be used in a communication with a third user.

Abstract: Systems, methods and non-transitory computer readable media for generating and modifying synthetic visual content using textual input are provided. One or more keywords may be received from a user. The one or more keywords may be used to generate a plurality of textual descriptions. Each generated textual description may correspond to a possible visual content. The generated plurality of textual descriptions may be presented to the user through a user interface that enables the user to modify the presented textual descriptions. A modification to at least one of the plurality of textual descriptions may be received from the user, therefore obtaining a modified plurality of textual descriptions. A selection of one textual description of the modified plurality of textual descriptions may be received from the user. A plurality of visual contents corresponding to the selected textual description may be presented to the user.

Abstract: Systems, methods and non-transitory computer readable media for generating visual content consistent with aspects of a visual brand language are provided. An indication of at least one aspect of a visual brand language may be received. Further, an indication of a desired visual content may be received. A new visual content consistent with the visual brand language and corresponding to the desired visual content may be generated based on the indication of the at least one aspect of the visual brand language and the indication of the desired visual content. The new visual content may be provided in a format ready for presentation.

Abstract: Methods, systems, and computer-readable storage media for processing images captured in a retail store and automatically identifying a product shortage are provided. In one implementation, a system may access information describing a desired placement of products on at least one store shelf. The system may analyze a first image captured at a first time to determine that a first quantity of products exceeds a quantity threshold. The system may analyze a second image captured at a second time later than the first time to determine that a second quantity of products is below the quantity threshold. The system may avoid initiating an unscheduled action to cause restocking until a third time later than the second time. Based on a determination that a third quantity of products at the third time are below the quantity threshold, the system may provide information indicative of a product shortage.

Abstract: A system for identifying products in a retail store is provided. The system may include a processor configured to: receive an image depicting a store shelf and a product displayed thereon; select a product model subset based on a characteristic of the store shelf determined based on analysis of the image; determine whether the product model subset is applicable to the product; and when the product model subset is applicable to the product, analyze a representation of the product depicted in the image using the product model subset, and identify the product based on the analysis of the representation of the product.

Abstract: A system for identifying perishable products in a retail store based on analysis of image data and for automatically generating suggestions relating to the identified products is provided. The system may comprise at least one processor configured to: receive a set of images depicting a plurality of perishable products displayed on at least one shelving unit in a retail store; analyze the set of images to determine information about a displayed inventory of the plurality of perishable products; obtain information about additional perishable products scheduled to be displayed on the at least one shelving unit; use the information about the displayed inventory and the information about the additional perishable products to determine at least one suggestion regarding placement of perishable products in the retail store; and provide the at least one suggestion to an entity associated with the retail store.

Abstract: Systems, methods and non-transitory computer readable media for generating looped video clips are provided. A still image may be received. The still image may be analyzed to generate a series of images. The series of images may include at least first, middle and last images. The first image may be substantially visually similar to the last image, and the middle image may be visually different from the first and last images. The series of images may be provided. Playing the series of images in a video clip that starts with the first image and finishes with the last image, and repeating the video clip from the first image immediately after completing the playing of the video clip with the last image may create visually smooth transaction in which the transition from the last image to the first image is visually indistinguishable from the transactions between frames within the video clip.

Abstract: The present disclosure relates to systems and methods for identifying products and monitoring planogram compliance using analysis of image data. In one implementation, the method may include accessing at least one planogram describing a desired placement of a plurality of product types on shelves of a plurality of retail stores; receiving image data from the plurality of retail stores; analyzing the image data to determine an actual placement of the plurality of product types on the shelves of the plurality of retail stores; determining at least one characteristic of planogram compliance based on detected differences between the at least one planogram and the actual placement of the plurality of product types on the shelves of the plurality of retail stores; and receiving checkout data from the plurality of retail stores reflecting sales of at least one product type from the plurality of product types.

Abstract: Systems, methods and non-transitory computer readable media for determining a degree of realism of an artificially generated visual content are provided. Artificially generated visual contents including a particular artificially generated visual content may be accessed. Captured visual contents may be accessed. For each person of a plurality of persons, a mix of visual contents including at least one artificially generated visual content and at least one captured visual content may be presented to the person, where the mix includes the particular artificially generated visual content. A reaction to the presentation indicative of whether the person believes that visual contents are artificially generated may be received. A degree of realism of the particular artificially generated visual content may be determined based on the reactions.

Abstract: Systems, methods and non-transitory computer readable media for identifying visual contents used for training of inference models are provided. A specific visual content may be received. Data based on at least one parameter of an inference model may be received. The inference model may be a result of training a machine learning algorithm using a plurality of training examples. Each training example of the plurality of training examples may include a visual content. The data and the specific visual content may be analyzed to determine a likelihood that the specific visual content is included in at least one training example of the plurality of training examples. A digital signal indicative of the likelihood that the specific visual content is included in at least one training example of the plurality of training examples may be generated.

Abstract: Systems, methods and non-transitory computer readable media for transforming non-realistic virtual environments to realistic virtual environments are provided. First digital signals representing virtual content in an extended reality environment may be received. The first digital signals may be used to identify a non-realistic portion of the virtual content. A generative model may be used to analyze the first digital signals to generate a realistic version of the identified non-realistic portion of the virtual content. Second digital signals configured to cause a wearable extended reality appliance to present the generated realistic version instead of the identified non-realistic portion of the virtual content in the extended reality environment may be generated.

Abstract: A system for processing images captured in a retail store and automatically identifying misplaced products is provided. The system may comprise at least one processor configured to receive one or more images captured by one or more image sensors from an environment of a retail store, detect in the one or more images a first product, determine that the first product is not located in the first correct display location, cause an issuance of a user-notification associated with the first product, detect in the one or more images a second product, determine that the second product is not located in the second correct display location, and after determining that the second product is not located in the second correct display location and when the second urgency level is lower than the first urgency level, withhold issuance of a user-notification associated with the second product.

Abstract: A system for acquiring images of products in a retail store is disclosed. The system may include a first housing configured for location on a retail shelving unit, and an image capture device included in the first housing and configured relative to the first housing such that an optical axis of the image capture device is directed toward an opposing retail shelving unit when the first housing is fixedly mounted on the retail shelving unit. The system may further include a second housing configured for location on the retail shelving unit separate from the first housing, the second housing containing a processor configured to control the image capture device and a network interface for communicating with a remote server. The system may further include a data conduit configured to enable transfer of control signals from the processor to the image capture device.

Abstract: A system for identifying perishable products in a retail store based on analysis of image data and for automatically generating suggestions relating to the identified products is provided. The system may comprise at least one processor configured to: receive a set of images depicting a plurality of perishable products displayed on at least one shelving unit in a retail store; analyze the set of images to determine information about a displayed inventory of the plurality of perishable products; obtain information about additional perishable products scheduled to be displayed on the at least one shelving unit; use the information about the displayed inventory and the information about the additional perishable products to determine at least one suggestion regarding placement of perishable products in the retail store; and provide the at least one suggestion to an entity associated with the retail store.

Abstract: A system for identifying perishable products in a retail store based on analysis of image data and for automatically generating suggestions relating to the identified products is provided. The system may comprise at least one processor configured to: receive a set of images depicting a plurality of perishable products displayed on at least one shelving unit in a retail store; analyze the set of images to determine information about a displayed inventory of the plurality of perishable products; obtain information about additional perishable products scheduled to be displayed on the at least one shelving unit; use the information about the displayed inventory and the information about the additional perishable products to determine at least one suggestion regarding placement of perishable products in the retail store; and provide the at least one suggestion to an entity associated with the retail store.

Abstract: A system for reducing product shortage durations in retail stores based on analysis of image data is provided. The system may comprise: a communication interface configured to receive image data from retail stores indicative of a product shortage of a product type relative to information describing a placement of products of a product type on a store shelf; and at least one processor configured to: analyze the image data to detect occurrences of product shortages of the product type in the retail stores and determine durations associated with the occurrences; identify a common factor contributing to the duration of part of the occurrences of the product shortages; determine an action, associated with the at least one common factor, for potentially reducing product shortage durations of future shortages of the product type in the retail stores; and provide information associated with the identified action to an entity.