Paper Info
Title
Cross-Modal Style Transfer
Abstract
We, humans, have the ability to easily imagine scenes that depict sentences such as "Today is a beautiful sunny day" or "There is a Christmas feel, in the air". While it is hard to precisely describe what one person may imagine, the essential high-level themes associated with such sentences largely remains the same. The ability to synthesize novel images that depict the feel of a sentence is very useful in a variety of applications such as education, advertisement, and entertainment. While existing papers tackle this problem given a style image, we aim to provide a far more intuitive and easy to use solution that synthesizes novel renditions of an existing image, conditioned on a given sentence. We present a method for cross-modal style transfer between an English sentence and an image, to produce a new image that imbibes the essential theme of the sentence. We do this by modifying the style transfer mechanism used in image style transfer to incorporate a style component derived from the given sentence. We demonstrate promising results using the YFCC100m dataset.
Year
Venue
Keywords
2018
2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Style Transfer, Cross-Modal, Multi-Modal, Image Retrieval, Visual Re-ranking
DocType
ISSN
Citations 
Conference
1522-4880
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Sahil Chelaramani100.34
Abhishek Jha254.81
Anoop M. Namboodiri325526.36