Abstract: A machine is configured to determine fashion preferences of users and to provide item recommendations based on the fashion preferences. For example, the machine accesses an indication of a fashion style of a user. The fashion style is determined based on automatically captured data pertaining to the user. The machine identifies, based on the fashion style, one or more fashion items from an inventory of fashion items. The machine generates one or more selectable user interface elements for inclusion in a user interface. The one or more user interface elements correspond to the one or more fashion items. The machine causes generation and display of the user interface that includes the one or more selectable user interface elements. A selection of a selectable user interface element results in display of a combination of an image of a particular fashion item and an image of an item worn by the user.

Abstract: In various example embodiments, a system and method for determining an item that has confirmed characteristics are described herein. An image that depicts an object is received from a client device. Structured data that corresponds to characteristics of one or more items are retrieved. A set of characteristics is determined, the set of characteristics being predicted to match with the object. An interface that includes a request for confirmation of the set of characteristics is generated. The interface is displayed on the client device. Confirmation that at least one characteristic from the set of characteristics matches with the object depicted in the image is received from the client device.

Abstract: Systems and methods to fit an image of an inventory part are described. The system receives, over a network, a selection that identities a part type and further receives, over the network, an image of a vehicle. The system antomatically identifies an image of a first inventory part based on the selection of the part type and the image of the vehicle. The system automatically positions two boundaries of a rectangle over the image of the vehicle based on the part type, the rectangle including an image of a first vehicle part. The system fits the image of the first inventory part over the image of the first vehicle part based on the rectangle. The system communicates, over the network, a user interface including the image of the vehicle including the image of the first inventory part fitted over the image of the first vehicle part.

Abstract: Methods, systems, and computer programs are presented for adding new features to a network service. An example method includes accessing an image from a user device to determine a salient object count of a plurality of objects in the image. A salient object count of the plurality of objects in the image is determined. An indicator of the salient object count of the plurality of objects in the image is caused to be displayed on the user device.

Abstract: Various embodiments use a neural network to analyze images for aspects that characterize the images, to present locations of those aspects on the images, and, additionally, to permit a user to interact with those locations on the images. For example, a user may interact with a visual cue over one of those locations to modify, refine, or filter the results of a visual search, performed on a publication corpus, that uses an input image (e.g., one captured using a mobile device) as a search query.

Abstract: In various example embodiments, a system and method for integration of a three-dimensional model is disclosed. In one example embodiment, a method includes receiving a plurality of images, selecting points on the images and triangulating the points to generate a plurality of depth maps, generate a three-dimensional mesh by combining the plurality of depth maps, generating a three-dimensional model of the item by projecting the plurality of images onto the mesh using the points, calibrating colors used in the model using colors diffuse properties of the colors in the images, and providing a user interface allowing a user to select one or more user points on the three-dimensional model and provide additional information associated with the selected user points.

Abstract: Vehicles and other items often have corresponding documentation, such as registration cards, that includes a significant amount of informative textual information that can be used in identifying the item. Traditional OCR may be unsuccessful when dealing with non-cooperative images. Accordingly, features such as dewarping, text alignment, and line identification and removal may aid in OCR of non-cooperative images. Dewarping involves determining curvature of a document depicted in an image and processing the image to dewarp the image of the document to make it more accurately conform to the ideal of a cooperative image. Text alignment involves determining an actual alignment of depicted text, even when the depicted text is not aligned with depicted visual cues. Line identification and removal involves identifying portions of the image that depict lines and removing those lines prior to OCR processing of the image.

Abstract: A machine may be configured to determine fashion preferences of users and to provide item recommendations to the users based on the users' fashion preferences. For example, the machine receives an image of a user and a set of spatial data indicating a position of the body of the user in a three-dimensional space. The machine may perform an analysis of the image and the set of spatial data. The performing of the analysis may include extracting, from the image, an image swatch that depicts a portion of an item worn by the user. The machine may identify a fashion preference of the user based on the analysis of the image and of the set of spatial data. The machine may identify an item that corresponds to the fashion preference of the user within an inventory of fashion items and may generate a recommendation of the identified fashion item.

Abstract: Disclosed are methods and systems for displaying items of clothing on a model having a similar body shape to that of an ecommerce user. In one aspects, a system includes one or more hardware processors configured to perform operations comprising receiving, by one or more hardware processors, an image, the image representing a user height, user weight, and user gender, causing display, by the one or more hardware processors, of a second image via a computer interface, the second image representing a model, the model selected based on a comparison of a model height, weight, and gender with the user height, weight, and gender respectively, receiving, by the one or more hardware processors, a selection of an item of clothing, and causing display, by the one or more hardware processors, of a representation of the selected model wearing the selected item of clothing.

Abstract: Methods, systems, and computer programs are presented for adding new features to a network service. A method includes receiving an image depicting an object of interest or selection of such an image. The selection acts an anchor for subsequently displayed item images.

Abstract: Apparatus and method for performing accurate text recognition of non-simplistic images (e.g., images with clutter backgrounds, lighting variations, font variations, non-standard perspectives, and the like) may employ a machine-learning approach to identify a discriminative feature set selected from among features computed for a plurality of irregularly positioned, sized, and/or shaped (e.g., randomly selected) image sub-regions.

Abstract: Systems, methods, and computer program products for identifying a candidate product in an electronic marketplace based on a visual comparison between candidate product image visual text content and input query image visual text content. Unlike conventional optical character recognition (OCR) based systems, embodiments automatically localize and isolate portions of a candidate product image and an input query image that each contain visual text content, and calculate a visual similarity measure between the respective portions. A trained neural network may be re-trained to more effectively find visual text content by using the localized and isolated visual text content portions as additional ground truths. The visual similarity measure serves as a visual search result score for the candidate product. Any number of images of any number of candidate products may be compared to an input query image to enable text-in-image based product searching without resorting to conventional OCR techniques.

Abstract: In various example embodiments, a system and method for projecting visual aspects into a vector space are presented. A query that includes visual data is received by the system from a client device. A visual aspect indicated in the visual data is analyzed. One or more symbols that correspond to the analyzed visual aspect is generated by the system. The analyzed visual aspect is projected into a vector space using the one or more symbols. A group of projections are identified, the group of projections being within a predetermined distance from the projected visual aspect in the vector space. An interface that depicts the further visual aspects is generated. The interface is displayed on the client device.

Abstract: Images uploaded to photo sharing websites often include some tags or sentence descriptions. In an example embodiment, these tags or descriptions, which might be relevant to the image contents, become the weak labels of these images. The weak labels can be used to identify concepts for the images using an iterative hard instance learning algorithm to discover visual concepts from the label and visual feature representations in the weakly labeled images. The visual concept detectors can be directly applied to concept recognition and detection.

Abstract: Embodiments of the present disclosure can be used to identify relationships between terms/words used in Internet search queries. Among other things, this helps systems provide Internet search results that are more useful and applicable to a given search query than conventional systems, thereby providing better content to users than conventional systems.

Abstract: Methods, systems, and computer programs are presented for adding new features to a network service. A method includes receiving an image depicting an object of interest. A category set is determined for the object of interest and an image signature is generated for the image. Using the category set and the image signature, the method identifies a set of publications within a publication database and assigns a rank to each publication. The method causes presentation of the ranked list of publications at a computing device from which the image was received.

Abstract: Example embodiments that analyze images to characterize aspects of the images rely on a same neural network to characterize multiple aspects in parallel. Because additional neural networks are not required for additional aspects, such an approach scales with increased aspects.

Abstract: Systems, methods, and computer program products for identifying a relevant candidate product in an electronic marketplace. Embodiments perform a visual similarity comparison between candidate product image visual content and input query image visual content, process formal and informal natural language user inputs, and coordinate aggregated past user interactions with the marketplace stored in a knowledge graph. Visually similar items and their corresponding product categories, aspects, and aspect values can determine suggested candidate products without discernible delay during a multi-turn user dialog. The user can then refine the search for the most relevant items available for purchase by providing responses to machine-generated prompts that are based on the initial search results from visual, voice, and/or text inputs. An intelligent online personal assistant can thus guide a user to the most relevant candidate product more efficiently than existing search tools.

Abstract: Example embodiments that analyze images to categorize images cluster the images within a same category. Images with mutual semantic similarity are in a same cluster. When an input image is compared to multiple clusters within a same category, there is an increased likelihood of accurate categorization of the input image.

Abstract: Systems, methods, and computer program products for identifying a candidate product in an electronic marketplace based on a visual comparison between candidate product image visual content and input query image visual content. Embodiments generate and store descriptive image signatures from candidate product images or selected portions of such images. A subsequently calculated visual similarity measure serves as a visual search result score for the candidate product in comparison to an input query image. Any number of images of any number of candidate products may be analyzed, such as for items available for sale in an online marketplace. Image analysis results are stored in a database and made available for subsequent automated on-demand visual comparisons to an input query image. The embodiments enable substantially real time visual based product searching of a potentially vast catalog of items.