  Generation of images from scene graphs is a promising direction towards
explicit scene generation and manipulation. However, the images generated from
the scene graphs lack quality, which in part comes due to high difficulty and
diversity in the data. We propose MIGS (Meta Image Generation from Scene
Graphs), a meta-learning based approach for few-shot image generation from
graphs that enables adapting the model to different scenes and increases the
image quality by training on diverse sets of tasks. By sampling the data in a
task-driven fashion, we train the generator using meta-learning on different
sets of tasks that are categorized based on the scene attributes. Our results
show that using this meta-learning approach for the generation of images from
scene graphs achieves state-of-the-art performance in terms of image quality
and capturing the semantic relationships in the scene. Project Website:
https://migs2021.github.io/

Computer Vision and Pattern Recognition;Machine Learning