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We propose a novel method for semantic segmentation, the task of labelingeach pixel in an image with a semantic class. Our method combines theadvantages of the two main competing paradigms. Methods based on regionclassification offer proper spatial support for appearance measurements, buttypically operate in two separate stages, none of which targets pixel labelingperformance at the end of the pipeline. More recent fully convolutional methodsare capable of end-to-end training for the final pixel labeling, but resort tofixed patches as spatial support. We show how to modify modern region-basedapproaches to enable end-to-end training for semantic segmentation. This isachieved via a differentiable region-to-pixel layer and a differentiablefree-form Region-of-Interest pooling layer. Our method improves thestate-of-the-art in terms of class-average accuracy with 64.0% on SIFT Flow and49.9% on PASCAL Context, and is particularly accurate at object boundaries.

Region-based semantic segmentation with end-to-end training