Visual Tracking via Deep Generative Models


Visual Tracking via Deep Generative Models – Deep neural networks have become a popular approach for machine learning and visual recognition applications. This makes it very difficult to optimize training with these models. The goal of this paper is to study the effect of modeling over training data using different deep models and learning techniques. We used a deep neural network (DNN) model and a stochastic gradient descent classifier to explore which models outperform and learn the best performance. We compared the performance of learning the model and the algorithm using simulated data in which we used a variety of datasets. Experimental results showed that the difference was substantial.

We present an efficient online learning strategy for predicting a target state. Our approach uses the information collected through a user’s interactions as an encoder and decoder. We derive a generalization to continuous relationship, i.e., a causal graph with a stationary (but in) and a non-linear (but in) model. We show how we can obtain a causal graph with continuous relationship for actions and actions with the same model. Extensive experiments using the MNIST dataset demonstrate the quality of our approach: we show that our approach outperforms the state-of-the-art approaches.

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Visual Tracking via Deep Generative Models

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  • Fast Convergence of Bayesian Networks via Bayesian Network Kernels

    A Generalisation to Generate Hidden Inter-relationships for Action LabelsWe present an efficient online learning strategy for predicting a target state. Our approach uses the information collected through a user’s interactions as an encoder and decoder. We derive a generalization to continuous relationship, i.e., a causal graph with a stationary (but in) and a non-linear (but in) model. We show how we can obtain a causal graph with continuous relationship for actions and actions with the same model. Extensive experiments using the MNIST dataset demonstrate the quality of our approach: we show that our approach outperforms the state-of-the-art approaches.


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