Recurrent Convolutional Neural Networks for Scene Parsing
| Type of publication: | Idiap-RR |
| Citation: | Pinheiro_Idiap-RR-22-2013 |
| Number: | Idiap-RR-22-2013 |
| Year: | 2013 |
| Month: | 6 |
| 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. |
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| Projects |
Idiap IM2 |
| Authors | |
| Added by: | [ADM] |
| Total mark: | 0 |
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