Ms. Siyun Wang Profile

Siyun Wang

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 1 August 2021 Presentation + Paper

Large-scale visual search and similarity for e-commerce

Gaurav Anand, Siyun Wang, Karl Ni

Proceedings Volume 11843, 118430S (2021) https://doi.org/10.1117/12.2594924

KEYWORDS: Visualization, Image retrieval, Data modeling, Taxonomy, Visual process modeling, Systems modeling, Nickel, Neural networks, Information visualization, Image visualization

Read Abstract +

Visual search and similarity can aid an e-commerce platform by providing appropriate recommendations where semantic labeling and associated metadata does not always exist. In this work, we detail the specifics of our system that powers visually similar recommendations. While a common approach leverages learned representation from common classification tasks using DNN’s, the crux of the problem are the labels and ontology that is applied in order for the DNN to functionally perform. Our proposed approach in production for a variety of products is to supply these recommendations based on a defined taxonomy through a hierarchy that has been carefully curated while additionally scaled up through our platform's natural crowd-sourcing interface. To scale the use of these taxonomies in production, we quantization schemes for retrieving approximate nearest neighbors after applying base transformations on the images using Apache Beam and Tensorflow Transform. The nearest neighbor retrievals are based on using a ResNet model architecture, trained from scratch on 3000+ classes. These are trained daily in a distributed fashion and optimizing data throughput. Finally, in order to verify the appropriateness, we use an extensive human evaluation pipeline and quality control. In this work, we share our product design learnings from the various attempts/experiments we conducted for a successful launch.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years