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We explore design principles for general pixel-level prediction problems,from low-level edge detection to mid-level surface normal estimation tohigh-level semantic segmentation. Convolutional predictors, such as thefully-convolutional network (FCN), have achieved remarkable success byexploiting the spatial redundancy of neighboring pixels through convolutionalprocessing. Though computationally efficient, we point out that such approachesare not statistically efficient during learning precisely because spatialredundancy limits the information learned from neighboring pixels. Wedemonstrate that stratified sampling of pixels allows one to (1) add diversityduring batch updates, speeding up learning; (2) explore complex nonlinearpredictors, improving accuracy; and (3) efficiently train state-of-the-artmodels tabula rasa (i.e., "from scratch") for diverse pixel-labeling tasks. Oursingle architecture produces state-of-the-art results for semantic segmentationon PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset,and edge detection on BSDS.

PixelNet: Representation of the pixels, by the pixels, and for the pixels