Semantic image segmentation, which becomes one of the key applications in image
processing and computer vision domain, has been used in multiple domains such as …

Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing
objects from an open set of categories in diverse environments. One way to address this …

Abstract We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based
framework for panoptic segmentation designed around clustering. It rethinks the existing …

Abstract Recently, Neural Architecture Search (NAS) has successfully identified neural
network architectures that exceed human designed ones on large-scale image …

This article does not describe a working system. Instead, it presents a single idea about
representation that allows advances made by several different groups to be combined into …

Spatial pyramid pooling module or encode-decoder structure are used in deep neural
networks for semantic segmentation task. The former networks are able to encode multi …

Semantic segmentation is a challenging task that addresses most of the perception needs of
intelligent vehicles (IVs) in an unified way. Deep neural networks excel at this task, as they …

Although having achieved great success in medical image segmentation, deep learning-
based approaches usually require large amounts of well-annotated data, which can be …

There are two major types of uncertainty one can model. Aleatoric uncertainty captures
noise inherent in the observations. On the other hand, epistemic uncertainty accounts for …