Cross-Modal Style Transfer
Title | Cross-Modal Style Transfer |
Publication Type | Conference Paper |
Year of Publication | 2018 |
Authors | Chelaramani, S., Jha, A., Namboodiri, A. M. |
Conference Name | 2018 25th IEEE International Conference on Image Processing (ICIP) |
Date Published | Oct. 2018 |
Publisher | IEEE |
ISBN Number | 978-1-4799-7061-2 |
Keywords | convolutional neural networks, Cross-Modal, data mining, Euclidean distance, image retrieval, Metrics, Multi-Modal, neural style transfer, Pipelines, Predictive Metrics, pubcrawl, resilience, Resiliency, Scalability, Semantics, style transfer, Visual Re-ranking, visualization |
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. |
URL | https://ieeexplore.ieee.org/document/8451734 |
DOI | 10.1109/ICIP.2018.8451734 |
Citation Key | chelaramani_cross-modal_2018 |