Learning Object-Class Segmentation with Convolutional Neural Networks

After successes at image classication, segmentation is the next step towards image understanding for neural networks. We propose a convolutional network architecture that includes innovative elements, such as multiple output maps, suitable loss functions, supervised pretraining, multiscale inputs, reused outputs, and pairwise class location lters. Ex- periments on three data sets show that our method performs on par with current in computer vision methods with regards to accuracy and exceeds them in speed.

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