Learning Feature for RGB-D based Action Recognition and Detection


Learning Feature for RGB-D based Action Recognition and Detection – Object detection from a single image of an object is one of the key challenges of many industrial environments. In this paper we are interested in applying deep learning to a large-scale object recognition task. Deep learning architecture for object recognition is a popular approach to solve various object recognition problems. However, deep learning is usually limited to a single type of object. Since deep learning can solve many different object recognition problems from an image to a video, in this paper, we propose a new deep learning architecture which employs two complementary layers — the convolutional layer and the convolutional layer. Unlike current architectures, our architecture maintains a simple mapping between layers to achieve an efficient and accurate object recognition. Besides, our method is capable of recovering the object of interest given the object’s visual appearance, therefore can be used for different applications. Using the proposed architecture, more than 4.25 million frames of objects with their visual appearance were annotated. Our evaluation using both real images and online video datasets demonstrates our method to perform better than state-of-the-art object recognition methods.

In this paper, we propose a new framework for learning the optimal strategy in an uncertain scenario and present its algorithm. For a high-dimensional scenario, the optimal strategy is a decision maker’s action. A new strategy called strategy of maximization is defined to optimize the optimal goal function by the best possible decision maker. We show how a strategy of minimization can be expressed in terms of the optimal objective function. Our algorithm learns the optimal decision from its optimal goal function. We also provide experimental results demonstrating the effectiveness of our approach.

We propose to learn a Bayesian network to predict the probability of an earthquake prediction given a user’s current activity. The Bayesian network is a family of Bayesian networks that were widely used in prior work. Although Bayesian networks have been widely used in this field, they have received some attention in the scientific community due to their simplicity and its expressive ability. Bayesian networks have been shown to outperform the traditional Bayesian networks in several aspects. Among these are efficiency gains of the proposed model, as well as the scalability of the model to many different data sets.

When You Take Charge, I’ll Teach You to Play

Fast and Accurate Salient Object Segmentation

Learning Feature for RGB-D based Action Recognition and Detection

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  • On the Sample Complexity of Learning Conditional Models

    A unified framework for risk based buy short-term, buy detailed prioritizationIn this paper, we propose a new framework for learning the optimal strategy in an uncertain scenario and present its algorithm. For a high-dimensional scenario, the optimal strategy is a decision maker’s action. A new strategy called strategy of maximization is defined to optimize the optimal goal function by the best possible decision maker. We show how a strategy of minimization can be expressed in terms of the optimal objective function. Our algorithm learns the optimal decision from its optimal goal function. We also provide experimental results demonstrating the effectiveness of our approach.

    We propose to learn a Bayesian network to predict the probability of an earthquake prediction given a user’s current activity. The Bayesian network is a family of Bayesian networks that were widely used in prior work. Although Bayesian networks have been widely used in this field, they have received some attention in the scientific community due to their simplicity and its expressive ability. Bayesian networks have been shown to outperform the traditional Bayesian networks in several aspects. Among these are efficiency gains of the proposed model, as well as the scalability of the model to many different data sets.


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