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Deep Convolutional Neural Networks (DCNNs) have recently shown state of theart performance in high level vision tasks, such as image classification andobject detection. This work brings together methods from DCNNs andprobabilistic graphical models for addressing the task of pixel-levelclassification (also called "semantic image segmentation"). We show thatresponses at the final layer of DCNNs are not sufficiently localized foraccurate object segmentation. This is due to the very invariance propertiesthat make DCNNs good for high level tasks. We overcome this poor localizationproperty of deep networks by combining the responses at the final DCNN layerwith a fully connected Conditional Random Field (CRF). Qualitatively, our"DeepLab" system is able to localize segment boundaries at a level of accuracywhich is beyond previous methods. Quantitatively, our method sets the newstate-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching71.6% IOU accuracy in the test set. We show how these results can be obtainedefficiently: Careful network re-purposing and a novel application of the 'hole'algorithm from the wavelet community allow dense computation of neural netresponses at 8 frames per second on a modern GPU.

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs