The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

Version 3.3 significantly improves the deep learning object segmentation capabilities to more accurately segment difficult-to-distinguish objects, such as cells or nuclei that are very close ...

Researchers have developed an autonomous electric vehicle prototype designed to navigate challenging urban roads. The vehicle ...

Other vision problems besides basic image classification that have been solved with deep learning include image classification with localization, object detection, object segmentation, image style ...

In this post, I will briefly review the deep learning architectures that help computers detect objects. Convolutional neural networks One of the key components of most deep learning–based ...

Thus, the team developed DeepWMH, an open-source, annotation-free WMH lesion segmentation tool targeted for removing the need for human annotation, while achieving high segmentation accuracy.

Future integration of the deep learning algorithm into standard system software would allow factory engineers to train networks that could then be run locally for inspection. There are many more ...