Ebay, Incorporated

Mohammadhadi Kiapour

Robinson Piramuthu

Ajinkya Gorakhnath Kale

Qiaosong Wang

Fan Yang

Shuai Zheng

Japjit Singh Tulsi

Licheng Yu

Nicholas Whyte

Padmapriya Gudipati

Timothy Keefer

Ashmeet Singh Rekhi

Md Atiq Ul Islam

Giridharan Iyengar

Omid Poursaeed

Luke Richard-Ivar Heine

Nandini Ramakrishnan

Christophe Boudet

Fred Aye Zaw, Jr

Vignesh Jagadeesh

Wei Di

Bryan Allen Plummer

Alberto Ordonez Pereira

Ravindra Surya Lanka

Suchitra Ramesh

Shane Lin

John Weigel

Fujun Zhong

Niaz Ahamed Khaja Nazimudeen

Kenneth Clark Crookston

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.

Parallel prediction of multiple image aspects

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.

Visual aspect localization presentation

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.

Intelligent online personal assistant with offline visual search database

A machine is configured to generate in real time personalized online banner images for users based on data pertaining to user behavior in relation to an image of a product. For example, the machine receives a user selection indicating one or more data features associated with the user. The one or more data features include a data feature pertaining to user behavior in relation to an image of a product. The machine generates, using a machine learning algorithm, a data representation of the machine learning algorithm based on the one or more data features including the data feature pertaining to user behavior in relation to the image of the product. The data representation includes one or more data features pertaining to one or more characteristics of online banner images. The machine generates an online banner image for the user based on the data representation.

Generating personalized banner images using machine learning

Camera platform techniques are described. In an implementation, a plurality of digital images and data describing times, at which, the plurality of digital images are captured is received by a computing device. Objects of clothing are recognized from the digital images by the computing device using object recognition as part of machine learning. A user schedule is also received by the computing device that describes user appointments and times, at which, the appointments are scheduled. A user profile is generated by the computing device by training a model using machine learning based on the recognized objects of clothing, times at which corresponding digital images are captured, and the user schedule. From the user profile, a recommendation is generated by processing a subsequent user schedule using the model as part of machine learning by the computing device.

Camera platform incorporating schedule and stature

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.

Image analysis and prediction based visual search

Aspects of the present disclosure relate to a network-based crowd assisted query system that includes a client device in communication with an application server executing the crowd assisted query system over a network. For example, the crowd assisted query system may be or include a group of one or more server machines. Users of the crowd assisted query system are presented with a graphical user interface (GUI) configured to receive queries that include data objects, wherein the data objects include representations of unidentified items of interest to the user. The data objects may include media content, such as graphical images as well as audio data, and in some example embodiments may further include text data describing the unidentified items.

Crowd assisted query system

Various embodiments described herein utilize multiple levels of generative adversarial networks (GANs) to facilitate generation of digital images based on user-provided images. Some embodiments comprise a first generative adversarial network (GAN) and a second GAN coupled to the first GAN, where the first GAN includes an image generator and at least two discriminators, and the second GAN includes an image generator and at least one discriminator. According to some embodiments, the (first) image generator of the first GAN is trained by processing a user-provided image using the first GAN. For some embodiments, the user-provided image and the first generated image, generated by processing the user-provided image using the first GAN, are combined to produce a combined image. For some embodiments, the (second) image generator of the second GAN is trained by processing the combined image using the second GAN.

Generating a digital image using a generative adversarial network

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.

Intelligent online personal assistant with multi-turn dialog based on visual search

Camera platform and object inventory control

Growing community of inventors

San Francisco, CA, United States of America

Mohammadhadi Kiapour