Microsoft Technology Licensing, LLC

Other

Nikita Astrakhantsev

Arun Sacheti

Parthasarathy Govindarajen

Yan Wang

Jun Liu

Xiaodong Fan

Diane Shambaugh

Avinash Vemuluru

Nektarios Ioannides

Dong Han Wang

Ravi Theja Yada

Liang Tang

Souvick Sarkar

Adam Jeffrey Curtis

Jeffrey Roger Devries

Alexander Jack Sunell

Alexandra Victoria Foote

Elena Usinskiene

Qianyue You

An image selector selects images for visual similarity in some aspects and visual distinction in other aspects. A user submits a query to a search engine. Images identified by the search engine as relevant to the query are clustered into clusters having at least one similar attribute. Selection mechanisms organize images selected from each cluster based on a similarity metric into image rows. A selection metric is calculated for each image row and a row to be displayed is selected based on the selection metric. In some embodiments, images representative of a subset of image rows are displayed to a user and responsive to selection of a representative image, the corresponding image row is displayed.

Automatic image selection for visual consistency

The present disclosure relates to systems, devices, and methods for a visual exploration of search engine results. The systems, devices, and methods may present, in response to receiving the search engine results for a query to perform a task, a search result webpage with a cascading waterfall layout that includes a plurality of images from a plurality of webpages included in the search results. The search result webpage may also present, adjacent to the plurality of images, structured data corresponding to a selected image from the plurality of images where the structured data includes details from the webpage to complete the task.

Image reranking and presentation for visual exploration

A computer-implemented technique is described herein for performing an image-based search that allows a user to create a custom query image that expresses the user's search intent. The technique generates the query image based on one or more input images and/or one or more information items that describe at least one desired characteristic of the query image. The technique then submits the query image to a search engine, and, in response, receives a set of candidate images that match the query image. In one implementation, the technique constructs the query image using a decoder neural network that operates on a mixed latent variable vector. In one approach, the technique uses a generative adversarial network (GAN) to produce the decoder neural network.

Techniques for modifying a query image

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Bellevue, WA, United States of America

Nikita Astrakhantsev