Autoplay Next video from Max length

Augmenting Solr’s NLP Capabilities with Deep-Learning Features to Match Images: Kumar Shubham

2,975 views · Published 1 August 2017 · 20:04 · Indexed 20 September 2026

Channel: Hasgeek TV · 2017 · Science & Technology

Matching images with human-like accuracy is typically extremely expensive. A lot of GPU resources and training data are required for the deep-learning model to perform image-matching. While GPU is something that most companies can afford, training data is hard to obtain.

At DataWeave, we crawl millions of products listed across e-commerce websites, and match them to deliver competitive insights to our clients. In the fashion vertical, however, text matching alone is insufficient to accurately match products, as product descriptions are usually not detailed enough.

We asked ourselves, is there any way of complementing information from product descriptions and titles to improve the accuracy of image-matching?

Solr is a popular text search engine known for its NLP capabilities. This talk will present an innovative way of storing deep-learning features in Solr, and augmenting Solr’s NLP capabilities to achieve elevated levels of accuracy in our product matching efforts.

Slides and more info: https://fifthelephant.talkfunnel.com/2017/80-augmenting-solrs-nlp-capabilities-with-deep-learni

Augmenting Solr’s NLP Capabilities with Deep-Learning Features to Match Images: Kumar Shubham

More from this channel