Ebay, Incorporated

Flashfoto, Inc.

Paypal, Inc.

Kla Tencor Corporation

Kla-tencor Technologies Corporation

Amazon Technologies, Inc.

Other

Robinson Piramuthu

Anurag Bhardwaj

Mohammadhadi Kiapour

Qiaosong Wang

Wei Di

Vignesh Jagadeesh

Neelakantan Sundaresan

Fan Yang

Ajinkya Gorakhnath Kale

Shuai Zheng

Elizabeth F Churchill

Martin Plihal

Patrick Huet

Japjit Singh Tulsi

Licheng Yu

Wen Zheng

Muhammad Raffay Hamid

Jinrong Xie

Yan Xiong

Nicholas Whyte

Padmapriya Gudipati

Ashmeet Singh Rekhi

Timothy Keefer

Md Atiq Ul Islam

Giridharan Iyengar

Kuang-chih Lee

Hassan Sawaf

Evgeny Matusov

Sanjika Hewavitharana

Daniel Prochazka

Omid Poursaeed

Cho Huak Teh

Christopher W Lee

James Thomas Dowdell

Mohammad Haris Baig

Gokhan Tur

George J Kren

Eliezer Rosengaus

Bryan Allen Plummer

Andrew Chalkley

Braddock Craig Gaskill

Paul J Sullivan

Bindia Saraf

Bryan Kent Ressler

Kevin Shude Lin

Alberto Ordonez Pereira

Ravindra Surya Lanka

Katharine Ip

Vicente Ordonez-Roman

Rohit Pandey

Mahyar Najibikohnehshahri

Chen-Yu Lee

Yang Liu

Nate L Lyman

Seokhwan Kim

Suchitra Ramesh

Shane Lin

John Weigel

Sandeep Bhagwat

Fujun Zhong

Niaz Ahamed Khaja Nazimudeen

Sanqiang Zhao

Eshan Bhatnagar

Paul King

Maruti Shanbhag

Peter Eldredge

Mark Rollins

Shaio Roan

Kai Liu

Kai Chi

Jiangbo Yuan

Gunnar Atli Sigurdsson

Taehwan Kim

Kota Hara

Zhicheng Yan

Dennis Decoste

Kenneth Clark Crookston

Bolei Zhou

Zixuan Wang

M Hadi Kiapour

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

Disclosed are embodiments for facilitating automatic-guided image capturing and presentation. In some embodiments, the method includes capturing an image of an item, removing a background of the image frame, performing manual mask editing, generating an item listing based on the manually edited mask, inferring item information from the image frame and applying the inferred item information to an item listing.

Automatic guided image capturing and presentation of images

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 system receives image data associated with an item, where the image data comprising a view of the item from two or more angles; determines physical attributes of the item; generates a base model of the item; samples the base model to generate one or more sampled models, each of the one or more sampled models comprising a subset of the geometric data, the subset of the geometric data determined based on one or more device characteristics of one or more user devices that interface with the system; receives device characteristics of a user device associated with a request from the user device for the item; selects, based on the received device characteristics, a sampled model of the item; and transmits a data object comprising the selected sampled model to the user device to cause the user device to generate a three-dimensional rendering of the item.

Reconstruction of 3D model with immersive experience

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.

Method, medium, and system for intelligent online personal assistant with image text localization

Techniques for rendering visual content, in response to one or more utterances, are described. A device receives one or more utterances that define a parameter(s) for desired output content. A system (or the device) identifies natural language data corresponding to the desired content, and uses natural language generation processes to update the natural language data based on the parameter(s). The system (or the device) then generates an image based on the updated natural language data. The system (or the device) also generates video data of an avatar. The device displays the image and the avatar, and synchronizes movements of the avatar with output of synthesized speech of the updated natural language data. The device may also display subtitles of the updated natural language data, and cause a word of the subtitles to be emphasized when synthesized speech of the word is being output.

Virtual conversational companion

Disclosed are systems, methods, and non-transitory computer-readable media for using adversarial learning for fine-grained image search. An image search system receives a search query that includes an input image depicting an object. The search system generates, using a generator, a vector representation of the object in a normalized view. The generator was trained based on a set of reference images of known objects in multiple views, and feedback data received from an evaluator that indicates performance of the generator at generating vector representations of the known objects in the normalized view. The evaluator including a discriminator sub-module, a normalizer sub-module, and a semantic embedding sub-module that generate the feedback data. The image search system identifies, based on the vector representation of the object, a set of other images depicting the object, and returns at least one of the other images in response to the search query.

Adversarial learning for finegrained image search

An apparatus and method to adjust item recommendations are disclosed herein. A first image attribute of a query image is compared to a second image attribute of each of a plurality of inventory images of a plurality of inventory items to identify the inventory items similar to the query image. Item recommendations comprising the identified inventory items in a first listing order are provided for display at a remote device. A second listing order of the identified inventory items is determined based on a user preference for a particular one of the identified inventory items. At least the second listing order is provided to the remote device for re-display of the item recommendations in accordance with the second listing order.

Item recommendations based on image feature data

A machine may be configured to perform image evaluation of images depicting items for sale and to provide recommendations for improving the images depicting the items to increase the sales of the items depicted in the images. For example, the machine accesses a result of a user behavior analysis. The machine receives an image of an item from a user device. The machine performs an image evaluation of the received image based on an analysis of the received image and the result of the user behavior analysis. The performing of the image evaluation may include determining a likelihood of a user engaging in a desired user behavior in relation to the received image. Then, the machine generates, based on the evaluation of the received image, an output that references the received image and indicates the likelihood of a user engaging in the desired behavior.

Image evaluation

Described herein is a system for tracking objects and performing dynamic entity resolution using image data. For example, the system may build an environment map and populate the map with objects present in the environment. As the devices move about the environment it may capture image data and, based on its position and/or configuration of its components, may determine updated locations of objects that move in the environment. Upon receiving a query from a user, based on the location of the objects relative to the device/user, the system can interpret gestures and voice commands to infer which object is specified by the voice command. To build the environment map, the system performs object detection to generate bounding boxes associated with an object, then clusters the bounding boxes into a three-dimensional (3D) object associated with 3D coordinates. As the system tracks the object using the 3D coordinates while maintaining two-dimensional (2D) information (e.g., bounding boxes and other features), the system can use existing 2D models to process objects in 3D.

Object tracking and entity resolution

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

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Oakland, CA, United States of America

Robinson Piramuthu