Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …

Text-conditioned image editing has recently attracted considerable interest. However, most
methods are currently limited to one of the following: specific editing types (eg, object …

We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …

We argue that the theory and practice of diffusion-based generative models are currently
unnecessarily convoluted and seek to remedy the situation by presenting a design space …

The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Abstract Diffusion Probabilistic Model (DPM) has recently become one of the hottest topics in
computer vision. Its image generation applications, such as Imagen, Latent Diffusion …

Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image.
As permutations of instance IDs are also valid solutions, the task requires learning of high …

Collective insights from a group of experts have always proven to outperform an individual's
best diagnostic for clinical tasks. For the task of medical image segmentation, existing …