Abstract: A system for processing images captured in a retail store is provided. The system may include a processor configured to: access a database storing a group of product models; receive an image depicting at least part of a store shelf having a plurality of products of a same type displayed thereon; analyze the image and determine a first candidate type of the products based on the group of product models and the image analysis; determine a first confidence level associated with the first candidate type; when the first confidence level is below a confidence threshold, determine a second candidate type of the products using contextual information; determine a second confidence level associated with the determined second candidate type of the plurality of products; and when the second confidence level is above the confidence threshold, initiate an action to update the group of product models stored in the database.

Abstract: Methods, systems, and computer-readable media are provided for selecting items for presentation on electronic visual displays in retail stores. In one implementation, a method may comprise: obtaining a plurality of images of products in a retail store; analyzing a first image to determine whether products of a particular product type are available at a first point in time; analyzing a second image to determine whether products of the particular product type are available at a second point in time; and based on the determination of whether products of the particular product type are available at the first point in time and the determination of whether products of the particular product type are available at the second point in time, selecting whether to display a particular item on an electronic visual display in the retail store.

Abstract: Methods, systems, and computer-readable media are provided for providing information on electronic visual displays in retail stores. In one implementation, a door for a retail storage container may include one or more electronic visual displays. In one implementation, the electronic visual display may be connected to a shelf in the retail store. In one implementation, an image of products in a retail store captured using at least one image sensor may be obtained, and the image may be analyzed to determine a condition of products of a particular product type. Further, based on the determined condition of the products of the particular product type, at least one display parameter may be selected for a particular item, and the selected at least one display parameter may be used to display the particular item on an electronic visual display in the retail store.

Abstract: Systems, methods and non-transitory computer readable media for propagating changes from one visual content to other visual contents are provided. A plurality of visual contents may be accessed. A first visual content and a modified version of the first visual content may be accessed. The first visual content and the modified version of the first visual content may be analyzed to determine a manipulation for the plurality of visual contents. The determined manipulation may be used to generate a manipulated visual content for each visual content of the plurality of visual contents. The generated manipulated visual contents may be provided.

Abstract: Systems, methods and non-transitory computer readable media for generating content using a generative model without relying on selected training examples are provided. An input indicative of a desire to generate a new content using a generative model 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 be associated with a respective content. Further, an indication of a particular subgroup of at least one but not all of the plurality of training examples may be obtained. Based on the indication, the input and the generative model may be used to generate the new content, abstaining from basing the generation of the new content on any training example included in the particular subgroup. The new content may be provided.

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: Systems, methods and non-transitory computer readable media for generating and orchestrating motion of visual contents are provided. A plurality of visual contents may be accessed. Data indicative of a layout of the plurality of visual contents in a user interface may be accessed. A sequence for the plurality of visual contents may be determined based on the layout. For each visual content of the plurality of visual contents, the visual content may be analyzed to generate a video clip including a motion of at least one object depicted in the visual content. A presentation of the plurality of visual contents in the user interface may be caused. The determined sequence for the plurality of visual contents may be used to orchestrate a series of playbacks of the generated video clips.

Abstract: Systems, methods and non-transitory computer readable media for propagating changes from one visual content to other visual contents are provided. A plurality of visual contents may be accessed. A first visual content and a modified version of the first visual content may be accessed. The first visual content and the modified version of the first visual content may be analyzed to determine a manipulation for the plurality of visual contents. The determined manipulation may be used to generate a manipulated visual content for each visual content of the plurality of visual contents. The generated manipulated visual contents may be provided.

Abstract: Methods, systems, and computer-readable media are provided for customized presentation of items on electronic visual displays in retail stores. In one implementation, a method may comprise: obtaining a plurality of images of products in a retail store captured; analyzing a first image to determine whether products of a particular product type are available at a first point in time; analyzing a second image to determine whether products of the particular product type are available at a second point in time; based on the determination of whether products of the particular product type are available at the first point in time and the determination of whether products of the particular product type are available at the second point in time, selecting at least one display parameter for a particular item; and using the selected at least one display parameter to display the particular item on an electronic visual display.

Abstract: Systems, methods and non-transitory computer readable media for attributing aspects of generated visual contents to training examples are provided. A first visual content generated using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Properties of an aspect of the first visual content and properties of visual contents associated with the plurality of training examples may be used to attribute the aspect of the first visual content to a subgroup of the plurality of training examples. For each source of the sources associated with the visual contents associated with the training examples of the subgroup, a data-record associated with the source may be updated based on the attribution of the aspect of the first visual content.

Abstract: A system for processing images captured in a retail store is provided. The system may include at least one processor configured to receive an image depicting a store shelf having at least one bottle displayed thereon and analyze the image to detect a representation in the image of the at least one bottle. The at least one bottle may have an outline design associated with a product shape. The at least one processor is also configured to identify in the image two outline elements being associated with the outline design of the at least one bottle. Each of the two outline elements may have a specific length. The at least one processor may further be configured to determine a size of the at least one bottle based on a comparison of the lengths of the two outline elements.

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

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: Systems, methods and non-transitory computer readable media for attributing aspects of generated visual contents to training examples are provided. A first visual content generated using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Properties of an aspect of the first visual content and properties of visual contents associated with the plurality of training examples may be used to attribute the aspect of the first visual content to a subgroup of the plurality of training examples. For each source of the sources associated with the visual contents associated with the training examples of the subgroup, a data-record associated with the source may be updated based on the attribution of the aspect of the first visual content.

Abstract: A method may include analyzing first images to determine an actual placement of first products displayed on shelves of a retail store; determining a deviation of an actual placement of some of the first products from the desired placement of products associated with a first planogram; receiving second images; analyzing the second images to determine an actual placement of the second products displayed on the shelves; identifying a deviation of the actual placement of some of the second products from the desired placement of products associated with the first planogram; using the second planogram to determine whether an arrangement associated with a second products conforms to the second planogram rather than to the first planogram; and when the arrangement associated with the second products conforms to the second planogram, avoiding issuance of a notification indicating a deviation relative to the first planogram.

Abstract: Systems, methods and non-transitory computer readable media for generating content using a generative model without relying on selected training examples are provided. An input indicative of a desire to generate a new content using a generative model 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 be associated with a respective content. Further, an indication of a particular subgroup of at least one but not all of the plurality of training examples may be obtained. Based on the indication, the input and the generative model may be used to generate the new content, abstaining from basing the generation of the new content on any training example included in the particular subgroup. The new content may be provided.

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

Abstract: Systems, methods and non-transitory computer readable media for prompt based background replacement are provided. A visual content including a background portion and at least one foreground object may be accessed. Further, a textual input indicative of a desire of an individual to modify the visual content may be received. The textual input and the visual content may be analyzed to generate a modified version of the visual content. The modified version may differ from the visual content in the background portion. Further, the modified version may include a depiction of the at least one foreground object substantially identical to a depiction of the at least one foreground object in the visual content. Further, a presentation of the modified version of the visual content to the individual may be caused.

Abstract: Systems, methods and non-transitory computer readable media for attributing aspects of generated visual contents to training examples are provided. A first visual content generated using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Properties of an aspect of the first visual content and properties of visual contents associated with the plurality of training examples may be used to attribute the aspect of the first visual content to a subgroup of the plurality of training examples. For each source of the sources associated with the visual contents associated with the training examples of the subgroup, a data-record associated with the source may be updated based on the attribution of the aspect of the first visual content.