The IEEE International Conference on Computer Vision received 4,303 papers and accepted 1,075 for the 2019 summit. Bellow is the best paper award.
Source: see paper listed below
Best Paper Award (Marr Prize): SinGAN: Learning a Generative Model from a Single Natural Image
Authors: Tamar Rott Shaham and Tomer Michaeli from the Israel Institute of Technology, and Tali Dekel, Google Research.
Abstract: We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.
More awards can be found here.
