  Natural Language Image Editing (NLIE) aims to use natural language
instructions to edit images. Since novices are inexperienced with image editing
techniques, their instructions are often ambiguous and contain high-level
abstractions that tend to correspond to complex editing steps to accomplish.
Motivated by this inexperience aspect, we aim to smooth the learning curve by
teaching the novices to edit images using low-level commanding terminologies.
Towards this end, we develop a task-oriented dialogue system to investigate
low-level instructions for NLIE. Our system grounds language on the level of
edit operations, and suggests options for a user to choose from. Though
compelled to express in low-level terms, a user evaluation shows that 25% of
users found our system easy-to-use, resonating with our motivation. An analysis
shows that users generally adapt to utilizing the proposed low-level language
interface. In this study, we identify that object segmentation as the key
factor to the user satisfaction. Our work demonstrates the advantages of the
low-level, direct language-action mapping approach that can be applied to other
problem domains beyond image editing such as audio editing or industrial
design.
