Amazon Technologies, Inc.

Michael Donoser

Barry James O'Brien

James Christopher Curlander

Marshall Friend Tappen

Pradeep Krishna Yarlagadda

Cédric Philippe Charles Jean Ghislain Archambeau

Loris Bazzani

Ralf Herbrich

Nikhil Garg

Eleonora Vig

Zohar Barzelay

Brad Bowman

Vernon Germano

Kripa Kanchana Sivakumar

Pablo Barbachano

Luitpold Staudigl

Muhammad Bilal Javed

Thomas Sydney Austin Wallis

Toma Belenzada

Matthew Norman

Bojan Pepik

Sabine Sternig

Matthieu Guillaumin

Eric Orme

Emily Evon McAninly

Prateek Ramesh Chandra Shah

Filip Saina

Erhan Gundogdu

Amaia Salvador Aguilera

Angel Noe Martinez Gonzalez

Benjamin Chung Yen Cheung

Yuxin Hou

Systems and techniques are generally described for attribute-based content selection and search. In some examples, a graphical user interface (GUI) may display an image of a first product comprising a plurality of visual attributes. In some further examples, the GUI may display at least a first control button with data identifying a first visual attribute of the plurality of visual attributes. In some cases, a first selection of the first control button may be received. In some examples, a first plurality of products may be determined based at least in part on the first selection of the first control button. The first plurality of products may be determined based on a visual similarity to the first product, and a visual dissimilarity to the first product with respect to the first visual attribute. In some examples, the first plurality of products may be displayed on the GUI.

Attribute-based content selection and search

Techniques are generally described for predicting text relevant to image data. In various examples, the techniques may include receiving image data comprising a first portion. The first portion of the image data may correspond to a first plurality of pixels when rendered on the display. Text data comprising a first text related to the first portion of the image data may be received. A first vector representation of the first portion of the image data may be determined. In some examples, a correspondence between the first portion of the image data and the first text may be determined based at least in part on the first vector representation. A first identifier of the first portion of image data may be stored in a data structure in association with a second identifier of the first text.

Relevant text identification based on image feature selection

Systems and techniques are generally described for generating visually blended recommendation grids. In some examples, a selection of a first item and a second item displayed on a display may be received. In various examples, the first item may be displayed in a first element of a grid and the second item may be displayed in a second element of the grid. In some examples, a third element of the grid that is disposed between the first element and the second element along an axis of the grid may be determined. In various examples, a third item may be determined for display in the third element of the grid based at least in part on a blended representation of an embedding of the first item and an embedding of the second item. The third item may be displayed in the third element of the grid.

Visual blending of content

In operating an ecommerce marketplace, pre-generated metadata is used to transform images after a request is received, in real-time, and with low latency. To accomplish the high-speed image transformation, an offline annotation of relevant image features is obtained using machine learning and metadata is stored based upon the results. The metadata is then used to perform the high-speed image transformation at request time. The transformations can include image cropping, adjustment in saturation, contrast, brightness, extracting portions of the image, etc. In one example, the metadata includes a bounding box giving coordinates of how to crop an image so that the image can be cropped without analyzing content or context of the image. Instead, to obtain the coordinates of the bounding box, content and context of the image are analyzed in pre-request processing. In this way, when the request is received, the more difficult and time-consuming processing is already completed.

Image cropping using pre-generated metadata

System and methods are provided for improved visual search systems that can use local features of images. The search system provides a user interface that enables a user to select areas or portions of an image to search on the selected features and the overall appearance of the image. The search system further provides customized user interfaces to exclude certain portions of images from the search algorithms. The search system can be used to detect potential intellectual property risks associated with items in an electronic catalog.

Feature based image detection

Techniques for prioritizing images associated with an item to display an appropriate image based on a query are described herein. For example, an attention score for an item attribute in an image of an item may be generated based at least in part on a model that uses one or more images of the item. The item attribute for the item associated with a query may be obtained. A plurality of items may be determined based at least in part on the item attribute being associated with the plurality of items where an individual item of the plurality of items includes a plurality of images of the item. The plurality of images of the individual item may be ranked based at least in part on corresponding attention scores associated with each image of the plurality of images.

Search result image selection techniques

Techniques are described for a content rating system that allows for automatic assignment of maturity ratings for media content.

Automated video ratings

Described are techniques for storing and retrieving items using a robotic device for moving items. Any combinations of image data depicting a manipulator interacting with an item, sensor data from sensors instrumenting the manipulator or item, item data regarding characteristics of the item, and constraint data relating to characteristics of the robotic device may be used to generate one or more configurations for the robotic device. The configurations may include points of contact and force vectors for contacting the item using the robotic device.

System for configuring a robotic device for moving items

Described are techniques for storing and retrieving items using a robotic manipulator. Images depicting a human interacting with an item, sensor data from sensors instrumenting the human or item, data regarding physical characteristics of the item, and constraint data relating to the robotic manipulator or the item may be used to generate one or more configurations for the robotic manipulator. The configurations may include points of contact and force vectors for contacting the item using the robotic manipulator.

System for configuring a robotic manipulator

Described are techniques for storing and retrieving items using a robotic manipulator. Images depicting a human interacting with an item, sensor data from sensors instrumenting the human or item, data regarding physical characteristics of the item, and constraint data relating to the robotic manipulator may be used to generate one or more configurations for the robotic manipulator. Points of contact and force vectors of the configurations may correspond to the points of contact and force vectors determined from the images and sensor data.

System for automating manipulation of items

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Berlin, Germany

Michael Donoser