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 show how to implement and perform a learning-based reinforcement learning (RL) system for learning an agent that can interactively search for products. This system is presented as a single agent in isolation from a game world. We develop a reinforcement learning approach that learns to find the relevant products that lead to product recommendations based on the customer-facing product portfolios. During the exploration phase, we provide a personalized recommendation sequence for the user, which we then learn using real-time reinforcement learning (RRL). We implement our system using reinforcement learning algorithms, which are evaluated by a community of researchers. We have evaluated our approach using different learning algorithms, which include reinforcement learning, reinforcement learning with a non-linear agent and a control agent. We have obtained state-of-the-art performance on a simulated benchmark dataset and in a benchmark dataset with an agent, which is composed of two agents. These experiments are reported on five benchmark datasets that simulate the behavior of an average-value optimization problem.

Kernel Mean Field Theory of Restricted Boltzmann Machines with Applications to Neural Networks

Video Encoding through Self-Attentional Deep Learning

Learning Feature for RGB-D based Action Recognition and Detection

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  • The Multi-Armed Bandit: Group ABA Training Meets Deep Learning

    Dynamic Stochastic Partitioning for Reinforcement Learning in Continuous-State Stochastic PartitionIn this paper, we show how to implement and perform a learning-based reinforcement learning (RL) system for learning an agent that can interactively search for products. This system is presented as a single agent in isolation from a game world. We develop a reinforcement learning approach that learns to find the relevant products that lead to product recommendations based on the customer-facing product portfolios. During the exploration phase, we provide a personalized recommendation sequence for the user, which we then learn using real-time reinforcement learning (RRL). We implement our system using reinforcement learning algorithms, which are evaluated by a community of researchers. We have evaluated our approach using different learning algorithms, which include reinforcement learning, reinforcement learning with a non-linear agent and a control agent. We have obtained state-of-the-art performance on a simulated benchmark dataset and in a benchmark dataset with an agent, which is composed of two agents. These experiments are reported on five benchmark datasets that simulate the behavior of an average-value optimization problem.


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