  Modern discriminative predictors have been shown to match natural
intelligences in specific perceptual tasks in image classification, object and
part detection, boundary extraction, etc. However, a major advantage that
natural intelligences still have is that they work well for "all" perceptual
problems together, solving them efficiently and coherently in an "integrated
manner". In order to capture some of these advantages in machine perception, we
ask two questions: whether deep neural networks can learn universal image
representations, useful not only for a single task but for all of them, and how
the solutions to the different tasks can be integrated in this framework. We
answer by proposing a new architecture, which we call "MultiNet", in which not
only deep image features are shared between tasks, but where tasks can interact
in a recurrent manner by encoding the results of their analysis in a common
shared representation of the data. In this manner, we show that the performance
of individual tasks in standard benchmarks can be improved first by sharing
features between them and then, more significantly, by integrating their
solutions in the common representation.
