Learning Dynamic Hierarchical Models for Anytime Scene Labeling
With increasing demand for efficient image and video analysis, test time cost of scene parsing becomes critical for many large scale or time sensitive vision applications. We propose a dynamic hierarchical model for anytime scene labeling that allows us to achieve flexible trade offs between efficiency and accuracy in pixel level prediction. In particular, our approach incorporates the cost of feature computation and model inference, and optimizes the model performance for any given test time budget by learning a sequence of image adaptive hierarchical models. We formulate this anytime representation learning as a Markov Decision Process with a discrete continuous state action space. A high quality policy of feature and model selection is learned based on an approximate policy iteration method with action proposal mechanism. We demonstrate the advantages of our dynamic non myopic anytime scene parsing on three semantic segmentation datasets, which achieves (90\ of the state of the art performances by using (15\ of their overall costs.
ECCV