Semantic segmentation, also called scene labeling, refers to the process of assigning a
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …

Image segmentation refers to the process to divide an image into meaningful non-
overlapping regions according to human perception, which has become a classic topic since …

Video frame interpolation aims to synthesize nonexistent frames in-between the original
frames. While significant advances have been made from the recent deep convolutional …

We present a novel and practical deep fully convolutional neural network architecture for
semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine …

During the last half decade, convolutional neural networks (CNNs) have triumphed over
semantic segmentation, which is a core task of various emerging industrial applications such …

Understanding the scene in which an autonomous robot operates is critical for its competent
functioning. Such scene comprehension necessitates recognizing instances of traffic …

We present a deep learning framework for probabilistic pixel-wise semantic segmentation,
which we term Bayesian SegNet. Semantic segmentation is an important tool for visual …

We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling.
SegNet has several attractive properties;(i) it only requires forward evaluation of a fully learnt …

Learning to reliably perceive and understand the scene is an integral enabler for robots to
operate in the real-world. This problem is inherently challenging due to the multitude of …

Exploiting synthetic data to learn deep models has attracted increasing attention in recent
years. However, the intrinsic domain difference between synthetic and real images usually …