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Found 6 papers, 1 papers with code
Dynamic Dual-Output Diffusion Models
In this paper, we reveal some of the causes that affect the generation quality of diffusion models, especially when sampling with few iterations, and come up with a simple, yet effective, solution to mitigate them.
Generating Correct Answers for Progressive Matrices Intelligence Tests
Raven's Progressive Matrices are multiple-choice intelligence tests, where one tries to complete the missing location in a $3\times 3$ grid of abstract images.
Scene Graph to Image Generation with Contextualized Object Layout Refinement
We empirically show on the COCO-STUFF dataset that our approach improves the quality of both the intermediate layout and the final image.
Scale-Localized Abstract Reasoning
First, it searches for relational patterns in multiple resolutions, which allows it to readily detect visual relations, such as location, in higher resolution, while allowing the lower resolution module to focus on semantic relations, such as shape type.
Evaluation Metrics for Conditional Image Generation
We present two new metrics for evaluating generative models in the class-conditional image generation setting.
OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering
We present a method for simultaneously learning, in an unsupervised manner, (i) a conditional image generator, (ii) foreground extraction and segmentation, (iii) clustering into a two-level class hierarchy, and (iv) object removal and background completion, all done without any use of annotation.
