2018/09/20 by K J Joseph, Joseph, K J, Arghya Pal +5
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1809.10238
To appear in the proceedings of IEEE Winter Conference on Applications of Computer Vision, WACV-2019
arxiv created 2018/09/20 · arxiv updated 2018/09/28
Generating an image from its description is a challenging task worth solving because of its numerous practical applications ranging from image editing to virtual reality. All existing methods use one single caption to generate a plausible image. A single caption by itself, can be limited, and may not be able to capture the variety of concepts and behavior that may be present in the image. We propose two deep generative models that generate an image by making use of multiple captions describing it. This is achieved by ensuring 'Cross-Caption Cycle Consistency' between the multiple captions and the generated image(s). We report quantitative and qualitative results on the standard Caltech-UCSD Birds (CUB) and Oxford-102 Flowers datasets to validate the efficacy of the proposed approach.