Convexity-Augmented Recurrent Networks for Real-Time Visual Tracking


Convexity-Augmented Recurrent Networks for Real-Time Visual Tracking – This work demonstrates the capability of object-based recurrent neural network (RNN) models to recover spatial-temporal data in image sequences, by learning a hierarchical, multi-dimensional recurrent network. In this work, we first evaluate the robustness of the hierarchical network to the presence of objects. Second, we study the performance of the recurrent network when it is trained using an object as input. Experiments on both static and dynamic scenes demonstrate that our network is able to recover accurate spatial-temporal temporal data in both image sequences and real world applications, by learning hierarchically hierarchical networks. The proposed network, which is a hierarchical recurrent network, achieves state-of-the-art speed on four synthetic datasets: CIFAR-10, CIFAR-100 and ImageNet.

The first part of the paper demonstrates the usefulness of the SIFT framework and illustrates the basic idea of optimizing the number of samples in the linearized Bayesian framework. Using the new technique, this algorithm can be successfully used for learning and for other applications of the linearization of Bayesian networks.

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Convexity-Augmented Recurrent Networks for Real-Time Visual Tracking

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    Bayesian Optimization for Nonparametric RegressionThe first part of the paper demonstrates the usefulness of the SIFT framework and illustrates the basic idea of optimizing the number of samples in the linearized Bayesian framework. Using the new technique, this algorithm can be successfully used for learning and for other applications of the linearization of Bayesian networks.


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