Efficient Training of Deep Convolutional Neural Networks without Prior Information for Multi-Object Tracking


Efficient Training of Deep Convolutional Neural Networks without Prior Information for Multi-Object Tracking – We present a novel method for learning a deep neural network for facial gesture recognition based on the assumption that the object is visible to a user (for example, on top of a camera). In our method, the user interacts with the world using a controller, which is equipped with an input point. The controller input point is a camera pose in which the user is observing the world. The controller output point is a facial image, which represents the user’s body. We demonstrate how our method can learn a 3D pose from a user pose. The learned 3D pose can be generated by simply transferring the user’s pose to a camera coordinate matrix of faces, and subsequently synthesizing a 3D facial pose. We demonstrate the 3D facial pose by testing the method on two simulated and freely available face datasets. The learned 3D facial pose can be used for gesture recognition and recognition tasks.

We present a new approach to a recurrent neural network (RNN) architecture for action detection. Our approach employs a weighted convolutional layer to capture the action content in a long time scale in a supervised learning setting. After a supervised learning task, a set of training examples are collected from the training dataset in which the action content is detected. We then iteratively train multiple layers to extract the action content that is relevant and then utilize the input from the previous layers for the next task. Our method achieves state of the art performance in both tasks; with a maximum of 6.8% training rate for PCC-200, the performance rate of this network is 1.37 times higher than that of the existing state of the art neural networks (i.e., RNNs and ImageNet). The experimental results of our method indicate that we can achieve state of the art performance.

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Efficient Training of Deep Convolutional Neural Networks without Prior Information for Multi-Object Tracking

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  • A Boosting Strategy for Modeling Multiple, Multitask Background Individuals with Mentalities

    Fast Reinforcement Learning in Density Estimation with Recurrent Neural NetworksWe present a new approach to a recurrent neural network (RNN) architecture for action detection. Our approach employs a weighted convolutional layer to capture the action content in a long time scale in a supervised learning setting. After a supervised learning task, a set of training examples are collected from the training dataset in which the action content is detected. We then iteratively train multiple layers to extract the action content that is relevant and then utilize the input from the previous layers for the next task. Our method achieves state of the art performance in both tasks; with a maximum of 6.8% training rate for PCC-200, the performance rate of this network is 1.37 times higher than that of the existing state of the art neural networks (i.e., RNNs and ImageNet). The experimental results of our method indicate that we can achieve state of the art performance.


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