Optical Flow Traces — A Computational Perspective


Optical Flow Traces — A Computational Perspective – Recurrent Neural networks (RNNs) provide effective features for image classification tasks, however they need to extract the information necessary to classify the data. The problem of classifying high-dimensional data based on structured convolutional features, such as RGBL images, is an important one. Here we propose a deep learning-based model which can extract the features and train them together. Experiments on a variety of datasets using RGBL data have demonstrated that even with a large amount of labeled data it is possible to significantly reduce the computational time compared to traditional methods. We also show that a deep RNN can yield good classification accuracy, thanks to the efficient use of convolutional neural networks for this purpose.

The recent success of deep learning in real-world problem solving was demonstrated for the sake of improving state-of-the-art model design and training for real world data. In this paper we propose a novel method to learn a representation for a probabilistic model to efficiently deal with uncertainty. Our results demonstrate that our model can learn representations of the probabilistic model, and our algorithm outperforms state-of-the-art models and models from the literature.

Automated prediction is a powerful tool to detect and to monitor the behavior behind a human activity in the wild. The goal of this work is to generate a predictive model of a player’s actions in a given scene in such a way that an effective prediction can be made. In this work we present a novel deep neural network model to learn the player’s behavior with a simple reinforcement learning framework. We demonstrate the importance of learning using our model by means of a realistic game.

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Optical Flow Traces — A Computational Perspective

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  • Robust SPUD: Predicting Root Mean Square (RMC) from an RGBD Image

    FlawBoost: Learning to Compare Models via Boosting on DistributionsThe recent success of deep learning in real-world problem solving was demonstrated for the sake of improving state-of-the-art model design and training for real world data. In this paper we propose a novel method to learn a representation for a probabilistic model to efficiently deal with uncertainty. Our results demonstrate that our model can learn representations of the probabilistic model, and our algorithm outperforms state-of-the-art models and models from the literature.

    Automated prediction is a powerful tool to detect and to monitor the behavior behind a human activity in the wild. The goal of this work is to generate a predictive model of a player’s actions in a given scene in such a way that an effective prediction can be made. In this work we present a novel deep neural network model to learn the player’s behavior with a simple reinforcement learning framework. We demonstrate the importance of learning using our model by means of a realistic game.


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