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 …

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 …

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

Semantic segmentation has made significant progress in recent years thanks to deep neural
networks, but the common objective of generating a single segmentation output that …

Deep learning (DL)-based methods have recently shown great promise in bitemporal
change detection (CD). Existing discriminative methods based on convolutional neural …

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 …

Camouflaged object detection is a challenging task that aims to identify objects that are
highly similar to their background. Due to the powerful noise-to-image denoising capability …

Segmentation is a fundamental problem in surgical scene analysis using artificial
intelligence. However, the inherent data scarcity in this domain makes it challenging to …

Abstract 3D object detection and pose estimation from a single-view image is challenging
due to the high uncertainty caused by the absence of 3D perception. As a solution recent …

Ultrasound (US) is generally preferred because it is of low-cost, safe, and non-invasive. US
image segmentation is crucial in image analysis. Recently, deep learning-based methods …