Background motion estimation in motion photos. By using the motion metadata from Gyro and OIS we are able to accurately classify features from the visual analysis into foreground and background.
Source: googleblog.com
Today, we are excited to announce the open source release of our latest and best performing semantic image segmentation model, DeepLab-v3+ [1], implemented in Tensorflow. This release includes DeepLab-v3+ models built on top of a powerful convolutional neural network (CNN) backbone architecture [2, 3] for the most accurate results, intended for server-side deployment. As part of this release, we are additionally sharing our Tensorflow model training and evaluation code, as well as models already pre-trained on the Pascal VOC 2012 and Cityscapes benchmark semantic segmentation tasks.
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In 2015, our early attempts to visualize how neural networks understand images led to psychedelic images. Soon after, we open sourced our code as DeepDream and it grew into a small art movement producing all sorts of amazing things. But we also continued the original line of research behind DeepDream, trying to address one of the most exciting questions in Deep Learning: how do neural networks do what they do?
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