no code implementations • CVPR 2022 • Yaniv Benny, Lior Wolf
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.
no code implementations • NeurIPS 2020 • Niv Pekar, Yaniv Benny, Lior Wolf
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.
no code implementations • 23 Sep 2020 • Maor Ivgi, Yaniv Benny, Avichai Ben-David, Jonathan Berant, Lior Wolf
We empirically show on the COCO-STUFF dataset that our approach improves the quality of both the intermediate layout and the final image.
2 code implementations • CVPR 2021 • Yaniv Benny, Niv Pekar, Lior Wolf
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.
no code implementations • 26 Apr 2020 • Yaniv Benny, Tomer Galanti, Sagie Benaim, Lior Wolf
We present two new metrics for evaluating generative models in the class-conditional image generation setting.
no code implementations • ECCV 2020 • Yaniv Benny, Lior Wolf
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.