Compositional GAN: Learning Image-Conditional Binary Composition

By Samaneh Azadi, Deepak Pathak, Sayna Ebrahimi, Trevor Darrell. 2018

103 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Adversarial Continual Learning (2020, methodology): “Adversarial learning has been used for different problems such as generative models [13], object composition [2], representation learning [22], domain adaptation [36], active learning [34], etc.”
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations (2019, background): “Azadi et al. [25] introduce a model to combine two objects or an object and a background scene in a sensible fashion, whereas van Steenkiste et al. [26] can generate scenes with an arbitrary number of components.”
Semantic Bottleneck Scene Generation (2019, background): “In conditional image synthesis one aims to generate images by conditioning on an input which can be provided in the form of an image [18, 47, 3, 5, 25], a text phrase [37, 45, 35, 2, 17], a scene graph [20, 2], a class label or a semantic lay-out [31, 8, 39, 32].”
Learning Object Placement by Inpainting for Compositional Data Augmentation (2020, background): “Azadi et al [1] proposed a self-consistent composition-by-decomposition network named Compositional GAN to composite a pair of objects.”
Benchmark for Compositional Text-to-Image Synthesis (2021, background): “For image synthesis, a few works aim at object-level compositionality such as adding a pair of sunglasses to a face [51, 3, 58].”
A Case for Object Compositionality in Deep Generative Models of Images (2018, background): “Azadi et al. (2018) considers a framework to generate images composed of two objects, conditioned on images of each single object.”