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.

In this article, an important question that concerns how to use word-level representations in machine translation is considered. The task is to discover the best sentence that can encode a given word for each word in the language’s context. Given a sentence and a set of sentences, a word-level representation has two functions. A word encoder is learned to encode the word’s meaning in the context. A word-level encoder is inferred to encode the sentence in the context. In the case of word-level models, a word-level encoding is also learned to produce the sentence in the context. This knowledge is used as a prior for subsequent inference, so that new words of the given sentence can be learned. The proposed model is evaluated using English-Urdu translation and a French-Urdu translation. The experiments show that the model can reach a better result with fewer parameters.

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

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  • Learning to recognize handwritten character ranges

    A Deep Learning Approach to Extracting Plausible Explanations From Chinese Handwriting TextsIn this article, an important question that concerns how to use word-level representations in machine translation is considered. The task is to discover the best sentence that can encode a given word for each word in the language’s context. Given a sentence and a set of sentences, a word-level representation has two functions. A word encoder is learned to encode the word’s meaning in the context. A word-level encoder is inferred to encode the sentence in the context. In the case of word-level models, a word-level encoding is also learned to produce the sentence in the context. This knowledge is used as a prior for subsequent inference, so that new words of the given sentence can be learned. The proposed model is evaluated using English-Urdu translation and a French-Urdu translation. The experiments show that the model can reach a better result with fewer parameters.


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