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The technique we use in Deep Mask is to think of segmentation as a very large number of binary classification problems.
First, for every (overlapping) patch in an image we ask: Does this patch contain an object?
Note that the system is not perfect yet, objects with red outlines are those annotated by humans but missed by Deep Mask.
Deep Mask knows nothing about specific object types, so while it can delineate both a dog and a sheep, it can’t tell them apart.
We’ve witnessed massive advances in image classification (what is in the image? But this is just the beginning of understanding the most relevant visual content of any image or video.
Over the past few years, progress in deep convolutional neural networks and the advent of ever more powerful computing architectures has led to machine vision systems rapidly increasing in their accuracy and capabilities.
The final stage of our recognition pipeline uses a specialized convolutional net, which we call Multi Path Net, to label each object mask with the object type it contains (e.g. As we continue improving these core technologies we’ll continue publishing our latest results and updating the open source tools we make available to the community. A machine sees none of this; an image is encoded as an array of numbers representing color values for each pixel, as in the second photo, the one on the right.
Let’s take a look at the building blocks of these algorithms. So how do we enable machine vision to go from pixels to a deeper understanding of an image?
For example, early layers in a deep net might capture edges and blobs, while upper layers tend to capture more semantic concepts such as the presence of an animal’s face or limbs.
By design, these upper-layer features are computed at a fairly low spatial resolution (for both computational reasons and in order to be invariant to small shifts in pixel locations).