ANALYZING STYLE TRANSFER ALGORITHMS FOR SEGMENTED IMAGES

Abstract

The recently developed Segment Anything Model has made grabbing semantically meaningful regions of an image easier than before. This will allow for new applications that build on this approach that weren’t previously possible. This thesis investigates integrating the Segment Anything Model with style transfer. Specifically, it proposes Partial Convolution as a way to improve style transfer for segmented regions. Additionally, it investigates how different style transfer techniques are affected by different mask sizes, image statistics, etc.