Semantic segmentation is an essential technology for self-driving cars to comprehend their
surroundings. Currently, real-time semantic segmentation networks commonly employ either …

Data augmentation is a crucial component of machine learning. In 2-D object detection
tasks, it can significantly enhance the performance of detectors without increasing the …

Real-time semantic segmentation of remote-sensing images demands a trade-off between
speed and accuracy, which makes it challenging. Apart from manually designed networks …

Autonomous driving requires a structured understanding of the surrounding road maps and
networks to navigate. However, considering the flexibility of autonomous vehicles and the …

The ability of recombinant, SARS-CoV-2 Spike (S) protein to modulate the production of two
COVID-19 relevant, pro-inflammatory cytokines (IL-6 and IFN-γ) in PBMC cultures of healthy …

Real-time semantic segmentation is one of the important tasks in the field of computer vision,
which is widely used in the fields of autonomous driving and medical imaging. Existing …

Low computational complexity and high segmentation accuracy are both essential to the
real-world semantic segmentation tasks. However, to speed up the model inference, most …

Unmanned aerial vehicle (UAV) remote sensing images used for semantic segmentation
possess distinct features compared to urban street scene images, including high resolution …

Recent advances in machine learning and computer vision promoted a surge in the
development of AI-based approaches aimed at improving various agricultural tasks. In this …

Unmanned aerial vehicle (UAV) orthoimages and digital surface models (DSMs) can
provide valuable insights for semantic segmentation methods in comprehending gully …