Reviews: PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph

Limited novelty: The proposed approach is closely related to two lines of related work: 1) sg2im [4] which generates images from scene graph representations, and 2) semi-parametric image synthesis [3], which leverages semantic layouts and training images to generate novel images. The key difference to sg2im is the use of image crops in order to perform semi-parametric synthesis; however, in comparison to prior work on semi-parametric methods [3], as suggested by the authors (Line 82-83) the primary difference is the use of graph convolution architecture, where a similar graph convolution method has been introduced in [4]. I'd like to see more justifications from the authors regarding the technical novelty of this approach in presence of these two lines of work. Limited resolution: My concern about the limited novelty is exacerbated by the fact that the generated images are still in low-resolution (64x64) as prior work [4], even though high-resolution image crops are used to aid the image generation process. In contrast, related work [3] is able to generate images of much higher resolutions, e.g., 512x1024, using their semi-parametric method (which was not compared in the experiment).