We propose a latent diffusion model with contrastive learning for audio-visual segmentation
(AVS) to extensively explore the contribution of audio. We interpret AVS as a conditional …

Conditional image generation plays a vital role in medical image analysis as it is effective in
tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models …

Absolute pose regression (APR) estimates global pose in an end-to-end manner achieving
impressive results in learn-based LiDAR localization. However compared to the top …

Existing learning-based solutions to medical image segmentation have two important
shortcomings. First for most new segmentation tasks a new model has to be trained or fine …

Denoising diffusion models have found applications in image segmentation by generating
segmented masks conditioned on images. Existing studies predominantly focus on adjusting …

Recovering degraded low-resolution text images is challenging especially for Chinese text
images with complex strokes and severe degradation in real-world scenarios. Ensuring both …

To fully understand the 3D context of a single image, a visual system must be able to
segment both the visible and occluded regions of objects, while discerning their occlusion …

In this work, we target the task of text-driven style transfer in the context of text-to-image (T2I)
diffusion models. The main challenge is consistent structure preservation while enabling …

Brain extraction, or the task of segmenting the brain in MR images, forms an essential step
for many neuroimaging applications. These include quantifying brain tissue volumes …

Data uncertainties, such as sensor noise or occlusions, can introduce irreducible
ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In …