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@TECHREPORT{Pinheiro_Idiap-RR-22-2013,
                      author = {Pinheiro, Pedro H. O. and Collobert, Ronan},
                    projects = {Idiap, IM2},
                       month = {6},
                       title = {Recurrent Convolutional Neural Networks for Scene Parsing},
                        type = {Idiap-RR},
                      number = {Idiap-RR-22-2013},
                        year = {2013},
                 institution = {Idiap},
                    abstract = {Scene parsing is a technique that consist on giving a label to all pixels in an image according to the class they belong to. To ensure a good visual coherence and a high class accuracy, it is essential for a scene parser to capture image long range dependencies. In a feed-forward architecture, this can be simply achieved by considering a sufficiently large input context patch, around each pixel to be labeled. We propose an approach consisting of a recurrent convolutional neural network which allows us to consider a large input context, while limiting the capacity of the model. Contrary to most standard approaches, our method does not rely on any segmentation methods, nor any task-specific features. The system is trained in an end-to-end manner over raw pixels, and models complex spatial dependencies with low inference cost. As the context size increases with the built-in recurrence, the system identifies and corrects its own errors. Our approach yields state-of-the-art performance on both the Stanford Background Dataset and the SIFT Flow Dataset, while remaining very fast at test time.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2013/Pinheiro_Idiap-RR-22-2013.pdf}
}