Learning to Map Between Domains
Abstract: Humans consume visual content avidly. We are in the midst of an imaging revolution enabled by inexpensive digital cameras and the internet. Almost everyone's cell phone has a camera. The photos taken by these cameras are shared massively and rapidly on the internet. However, there is an asymmetry: Each individual can consume only limited visual content in his limited lifetime, such that only a chosen few are talented enough to both express and understand something unseen visually and effectively. The rest of us try to understand and express something unseen by translating them to something seen before. In this dissertation, we investigate a number of data-driven approaches for mapping from an 'unseen' domain to a 'seen' domain. This work includes mapping between two image domains and mapping from an image domain to a language domain, which in computer vision are called, respectively, image-to-image translation and image captioning. The presented methods not only help users to easily and accurately synthesize useful photos, but also enable new visual and linguistic effects not possible before this work.
