Feature-Augmented Visuomotor Learning for Accurate Identification of Manipulating Objects


Feature-Augmented Visuomotor Learning for Accurate Identification of Manipulating Objects – This paper describes a simple, yet effective technique to detect object-specific behaviors from deep networks of object-sensitive photometric sensors. An attention mechanism is designed to guide object detection by leveraging photometric information provided by object features. The attention mechanism is implemented by using a deep convolutional neural network (CNN) to map photometric patterns from the input to the target object features. The learned network is then used to learn a visual interpretation of the photometric features. We show that the proposed method outperforms the state-of-the-art tracking approaches. On the other hand, our proposed method is capable of achieving higher accuracy when compared to state-of-the-art object detection approaches.

This work addresses the problem of learning a representation of natural language from text. This task is very challenging in some ways, such as due to the difficulty of choosing suitable models for learning a representation from text. In this work, we propose a novel model learning method that exploits the diversity of the natural language, and generalizes a number of its properties. The goal of learning a representation from text is to learn a representation of natural language that is different from the words, words, or sentences that are used by humans. To illustrate and motivate our method, we investigate its effectiveness on several real language datasets, as well as how it is possible to learn a representation from text from a machine translation corpus. The method is evaluated on five public datasets, where it outperforms the previous state-of-the-art methods.

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Feature-Augmented Visuomotor Learning for Accurate Identification of Manipulating Objects

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  • 3D Face Recognition with Convolutional Neural Networks using Fuzzy Generative Adversarial Networks

    On the role of evolutionary processes in the evolution of languageThis work addresses the problem of learning a representation of natural language from text. This task is very challenging in some ways, such as due to the difficulty of choosing suitable models for learning a representation from text. In this work, we propose a novel model learning method that exploits the diversity of the natural language, and generalizes a number of its properties. The goal of learning a representation from text is to learn a representation of natural language that is different from the words, words, or sentences that are used by humans. To illustrate and motivate our method, we investigate its effectiveness on several real language datasets, as well as how it is possible to learn a representation from text from a machine translation corpus. The method is evaluated on five public datasets, where it outperforms the previous state-of-the-art methods.


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