Abstract Corrosion costs an estimated 3–4% of GDP for most nations each year, leading to
significant loss of assets. Research regarding automatic corrosion detection is ongoing, with …

Diffusion models have shown impressive performance for generative modelling of images.
In this paper, we present a novel semantic segmentation method based on diffusion models …

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our
roads. While the perception of autonomous vehicles performs well under closed-set …

Image segmentation is the task of associating pixels in an image with their respective object
class labels. It has a wide range of applications in many industries including healthcare …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …

Abstract This paper presents Probabilistic Video Contrastive Learning, a self-supervised
representation learning method that bridges contrastive learning with probabilistic …

Fusing probabilistic information is a fundamental task in signal and data processing with
relevance to many fields of technology and science. In this work, we investigate the fusion of …

Species often adopt various camouflage strategies to be seamlessly blended into the
surroundings for self-protection. To figure out the concealment, predators have evolved …

Abstract Weakly-Supervised Semantic Segmentation (WSSS) segments objects without
heavy burden of dense annotation. While as a price, generated pseudo-masks exist obvious …

Abstract Open Set Recognition (OSR) has been an emerging topic. Besides recognizing
predefined classes, the system needs to reject the unknowns. Prototype learning is a …