Datasets and codes for image composition are summarized at https://github. com/bcmi/Awesome-Image-Composition.
In this work, we propose a novel Delta Generative Adversarial Network (DeltaGAN), which consists of a reconstruction subnetwork and a generation subnetwork.
In this paper, we propose a Fusing-and-Filling Generative Adversarial Network (F2GAN) to generate realistic and diverse images for a new category with only a few images.
To address these issues, we propose our MTL with Selective Augmentation (MTL-SA) method to select the training samples in unlabeled datasets with confident pseudo labels and close data distribution to the labeled dataset.
Matching generator can match random vectors with a few conditional images from the same category and generate new images for this category based on the fused features.