We propose a method for editing images from human instructions: given an input image and
a written instruction that tells the model what to do, our model follows these instructions to …

Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

Deep generative models are becoming increasingly powerful, now generating diverse high
fidelity photo-realistic samples given text prompts. Have they reached the point where …

Recent image generation models such as Stable Diffusion have exhibited an impressive
ability to generate fairly realistic images starting from a simple text prompt. Could such …

Generative adversarial networks (GANs) have drawn enormous attention due to their simple
yet effective training mechanism and superior image generation quality. With the ability to …

Text-to-image diffusion models have made significant advances in generating and editing
high-quality images. As a result, numerous approaches have explored the ability of diffusion …

Diffusion models have been shown to be capable of generating high-quality images,
suggesting that they could contain meaningful internal representations. Unfortunately, the …

Many fine-grained classification tasks, like rare animal identification, have limited training
data and consequently classifiers trained on these datasets often fail to generalize to …

Current perceptual similarity metrics operate at the level of pixels and patches. These
metrics compare images in terms of their low-level colors and textures, but fail to capture mid …

Abstract Generative Adversarial Networks (GANs) have established themselves as a
prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study …