Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to mapping the structure and function of the brain …

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 …

Current deep networks are very data-hungry and benefit from training on large-scale
datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data …

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 …

The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of
computer vision, thanks to its image generation applications, such as Imagen, Latent …

We propose a simple, efficient, yet powerful framework for dense visual predictions based
on the conditional diffusion pipeline. Our approach follows a" noise-to-map" generative …

Temporal action segmentation is crucial for understanding long-form videos. Previous works
on this task commonly adopt an iterative refinement paradigm by using multi-stage models …

Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …

Volume-wise labeling in 3D medical images is a time-consuming task that requires
expertise. As a result, there is growing interest in using semi-supervised learning (SSL) …