Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc. Hover, a major advantage that natural intelligences still have is that they work ll 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,  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.  ansr by proposing a new architecture, which  call multinet, in which not only deep image features are shared beten 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,  show that the performance of individual tasks in standard benchmarks can be improved first by sharing features beten them and then, more significantly, by integrating their solutions in the common representation.
['Hakan Bilen', 'Andrea Vedaldi']
I am writing a research paper on a idea Integrated perception with recurrent multi-task neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Integrated perception with recurrent multi-task neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc. Hover, a major advantage that natural intelligences still have is that they work ll 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,  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.  ansr by proposing a new architecture, which  call multinet, in which not only deep image features are shared beten 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,  show that the performance of individual tasks in standard benchmarks can be improved first by sharing features beten them and then, more significantly, by integrating their solutions in the common representation."}]