Google once again shows its impressive advanced AI/Deep Learning capabilities. Which made me recall that it is often the 'semantic', or meaning in context aspects that are most important for an AI or analytic method to be useful. And that assigning tags also implies we will need to maintain the tags as context changes. Below is technical, look at the link for some image examples that make this clearer.
Semantic Image Segmentation with DeepLab in Tensorflow
Posted by Liang-Chieh Chen and Yukun Zhu, Software Engineers, Google Research
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation. Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection. ... "
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment