Walmart Apollo, LLC

Abon Chaudhuri

Alessandro Magnani

Venkatesh Kandaswamy

Shreyansh Prakash Gandhi

Abhinandan Krishnan

Samrat Kokkula

Theban Stanley

Abilash Amarthaluri

Omer Ovenc

Behzad Ahmadi

Qian Li

Yanxin Pan

Swagata Chakraborty

Aakash Mayur Mehta

Edison Mingtao Zhang

Kyu Bin Kim

Ajinkya More

Nikash Walia

Krishna Chaitanya Yalamanchili

Paolo Messina

Nihar Vanjara

Bo Ling

Ashley Kim

Benjamin Matthew Breuner

A system including one or more processors and one or more non-transitory computer-readable storage media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: training a neural network detection model with a training dataset comprising synthetic training images by: using a transformation algorithm to create the synthetic training images by appending edge case training images to one or more compliant images; receiving, at the neural network detection model, as trained, at least one image; and determining, using the neural network detection model, as trained, whether the at least one image comprises non-compliant content. Other embodiments are disclosed herein.

Systems, methods, and techniques for training neural networks and utilizing the neural networks to detect non-compliant content

A method including obtaining image data and attribute information of a first item in an item catalog. The method also can include generating candidate variant items from the item catalog for the first item using a combination of (a) a k-nearest neighbors approach to search for first candidate variant items based on text embeddings for the attribute information of the first item, and (b) an elastic search approach to search for second candidate variant items based on image embeddings for the image data of the first item. The method additionally can include performing respective classifications based on respective pairs comprising the first item and each of the candidate variant items to filter the candidate variant items. The method further can include determining a respective distance between the first item and each of the candidate variant items, as filtered. The method additionally can include determining one or more items in the candidate variant items, as filtered, to include in a variant group for the first item, based on a decision function using a predetermined threshold and the respective distance for the each of the candidate variant items, as filtered. Other embodiments are described.

Automatically determining items to include in a variant group

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images; identifying a foreground of the one or more digital images; analyzing the foreground of the one or more digital images to identify a skin region in the foreground of the one or more digital images; when the skin region is identified, clustering a non-skin remainder of the foreground of the one or more digital images into one or more clusters; extracting one or more patches of the one or more digital images from the one or more clusters of the foreground of the one or more digital images; determining one or more scores for the one or more patches of the one or more digital images; and coordinating displaying a patch of the one or more patches on an electronic display based on the one or more scores for the one or more patches. Other embodiments are disclosed herein.

Systems and methods for extracting patches from digital images

A systems including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: receiving a query image comprising at least one target object and at least one candidate image; generating a plurality of proposals based on the at least one candidate image; selecting a portion of the plurality of proposals to produce a reduced proposal set; generating a query embedding corresponding to the query image; generating candidate embeddings corresponding to the portion of the plurality of proposals included in the reduced proposal set; computing similarity scores for the portion of the plurality of proposals; and comparing the similarity scores to a threshold to determine if the at least one candidate image comprises the at least one target object. Other embodiments are disclosed herein.

Systems and methods for executing attention-based object searches on images using neural network architectures

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform obtaining a set of items that have been grouped together as matching items in a group; performing an ensemble mismatch detection; performing multiple detection models on the set of items to generate respective outputs regarding mismatches; combining the respective outputs to determine whether a quantity of detected mismatches is at least a predetermined threshold; when the quantity of detected mismatches is at least the predetermined threshold, the acts also can include separating at least one of the set of items from the group; and when the quantity of detected mismatches is not at least the predetermined threshold, the acts additionally can include maintaining each item of the set of items in the group. Other embodiments are disclosed.

Mismatch detection model

A system can include one or more processors and one or more non-transitory computer-readable storage media storing computing instructions configured to run on the one or more processors and perform: generating a training dataset for training a neural network detection model; identifying, using the neural network detection model, as trained, the non-compliant content in the synthetic training images; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image comprises the non-compliant content. Other embodiments are disclosed herein.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image.

Systems and methods for identifying items in a digital image

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform receiving a title of an item associated with an online catalog; interpreting, using a natural language model, one or more attributes of the predetermined set of attributes; determining a first title quality score for the title based on a first rule; determining a second title quality score for the title based on a second rule; determining an aggregated title quality score for the title based on at least the first title quality score and the second title quality score; generating a content quality list for the title; and sending instructions to display, on a user interface of an electronic device, a content quality dashboard comprising the content quality list for the title of the item. Other embodiments are disclosed.

Systems and methods for a title quality scoring framework

A product image evaluation system. In embodiments, the system comprises or interacts with a product database comprising a product information record that comprises a product identifier and a product category for a product, and an image database comprising a plurality of candidate images for the product. In embodiments the image database can comprise images received from a plurality of different sources. The system can comprise a parameterized grouping engine configured to separate images into groups of similar images, an image selector configured to select one or more images from each group, and an image sorter configured to determine an order of the selected images. Embodiments can distill the superset of all available images to provide a set of images that are 'sufficiently different' from each other and satisfy quality requirements. As a result, no images containing unique information are left behind, and images containing duplicate or irrelevant information are discarded.

Product image evaluation system and method

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices.

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: generating a training dataset comprising synthetic training images for training a neural network detection model to identify non-compliant content in images; executing a training procedure that utilizes the synthetic training images to train the neural network detection model to identify the non-compliant content; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image includes the non-compliant content. Other embodiments are disclosed herein.

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving an image at a first-level analysis component comprising a first neural network structure; analyzing, using the first neural network structure of the first-level analysis component, the image to determine an image category associated with the image; selecting at least one second-level analysis component that is associated with the image category to analyze the image; analyzing, using a second neural network structure associated with the at least one second-level analysis component that was selected, the image to determine whether the image includes non-compliant content; and in response to determining that the image includes non-compliant content, executing a corrective measure. Other embodiments are disclosed herein.

Growing community of inventors

Sunnyvale, CA, United States of America

Abon Chaudhuri