The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

Medical image analysis helps in resolving clinical issues by examining clinically generated
images. In today's world of deep learning (DL) along with advances in computer vision, the …

Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …

A recurrent structure is a popular framework choice for the task of video super-resolution.
The state-of-the-art method BasicVSR adopts bidirectional propagation with feature …

We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We
start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution …

Finetuning a pretrained backbone in the encoder part of an image transformer network has
been the traditional approach for the semantic segmentation task. However, such an …

Semantic segmentation of remotely sensed imagery plays a critical role in many real-world
applications, such as environmental change monitoring, precision agriculture …

Semantic segmentation from very fine resolution (VFR) urban scene images plays a
significant role in several application scenarios including autonomous driving, land cover …

Real-time semantic segmentation is widely used in autonomous driving and robotics. Most
previous networks achieved great accuracy based on a complicated model involving mass …

In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial
context (large receptive fields) and fine spatial details (high resolution), both of which incur …