HOG: Histogram of Goals for Human Pose Translation from Natural and Vision-based Visualizations


HOG: Histogram of Goals for Human Pose Translation from Natural and Vision-based Visualizations – While a great deal has been made of the fact that human gesture identification was a core goal of visual interfaces in recent centuries, it has been less explored due to lack of high-level semantic modeling for each gesture. In this paper, we address the problem of human gesture identification in text images. We present a method for the extraction of human gesture text via a visual dictionary. The word similarity map is presented to the visual dictionary, which is a sparse representation of human semantic semantic information. The proposed recognition method utilizes the deep convolutional neural network (CNN) to classify human gestures. Through this work we propose the deep CNN to recognize human gesture objects. Our method achieves recognition rate of up to 2.68% on the VisualFaces dataset, which is an impressive performance.

We present a general method for learning feature representations from the knowledge-base of an underlying Bayesian network. Our method consists of two steps. First, a new feature distribution over the data is generated which is used to estimate the posterior distribution of the Bayesian network. Since each new feature is a feature vector, the prior distribution of each vector can be computed on the data by the distribution associated with the feature distribution. We can then represent the posterior distribution as a Bayesian network. We study the learning capacity of a model of an underlying Bayesian network. On a machine learning dataset, we train a deep network with a recurrent neural network (RNN) to estimate the posterior distribution of the network. Experiments show that the system outperforms previous state-of-the-art Bayesian networks by a large margin. Additionally, we demonstrate that neural network-based representations are much more interpretable than regular Bayesian networks.

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HOG: Histogram of Goals for Human Pose Translation from Natural and Vision-based Visualizations

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  • Guided Depth Estimation

    The Role of Recurrence and Other Constraints in Bayesian Deep Learning Models of Knowledge MapsWe present a general method for learning feature representations from the knowledge-base of an underlying Bayesian network. Our method consists of two steps. First, a new feature distribution over the data is generated which is used to estimate the posterior distribution of the Bayesian network. Since each new feature is a feature vector, the prior distribution of each vector can be computed on the data by the distribution associated with the feature distribution. We can then represent the posterior distribution as a Bayesian network. We study the learning capacity of a model of an underlying Bayesian network. On a machine learning dataset, we train a deep network with a recurrent neural network (RNN) to estimate the posterior distribution of the network. Experiments show that the system outperforms previous state-of-the-art Bayesian networks by a large margin. Additionally, we demonstrate that neural network-based representations are much more interpretable than regular Bayesian networks.


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