Learning from Continuous Events with the Gated Recurrent Neural Network


Learning from Continuous Events with the Gated Recurrent Neural Network – We present a novel deep-learning technique to automatically learn the spatial location of objects in a scene, which is based on Recurrent Neural Networks (RNN) and can achieve high accuracies by learning the object location from a large set of object instances. In this work, we provide state-of-the-art classification accuracies at an accuracy of 10.81%. Our method can be embedded into many different RNN architectures and can be applied to datasets. We demonstrate the effectiveness of our approach in a supervised task where we use Gated Recurrent Neural Network (GRNN) to extract object-oriented objects and then apply the method at the scene.

We present a novel dataset of 3D human pose images. The dataset is comprised of about 12K human pose images, and can be used to improve the recognition performance in different applications. Our dataset consists of 3D human pose images that are retrieved from a public dataset consisting of images from different subjects. We also collect the input from human subjects and train Convolutional Neural Networks to model the pose of people in various pose datasets for various face recognition tasks. We evaluate our method using four benchmark datasets, including a face recognition dataset, a human poses dataset, and a pose benchmark dataset. We report how our dataset enables various face recognition applications including face detection and person re-identification, and how we were able to obtain state-of-the-art classification performance on this dataset. For our dataset, we created a deep learning algorithm to generate human pose images. However, our CNN architecture does not support the multi-view training model, yet in these two datasets, and our network provides the same performance to 3D human pose images as it does for a generic pose model.

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Learning from Continuous Events with the Gated Recurrent Neural Network

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  • Exploiting Entity Understanding in Deep Learning and Recurrent Networks

    Learning Non-Linear Image Classification for Visual TrackingWe present a novel dataset of 3D human pose images. The dataset is comprised of about 12K human pose images, and can be used to improve the recognition performance in different applications. Our dataset consists of 3D human pose images that are retrieved from a public dataset consisting of images from different subjects. We also collect the input from human subjects and train Convolutional Neural Networks to model the pose of people in various pose datasets for various face recognition tasks. We evaluate our method using four benchmark datasets, including a face recognition dataset, a human poses dataset, and a pose benchmark dataset. We report how our dataset enables various face recognition applications including face detection and person re-identification, and how we were able to obtain state-of-the-art classification performance on this dataset. For our dataset, we created a deep learning algorithm to generate human pose images. However, our CNN architecture does not support the multi-view training model, yet in these two datasets, and our network provides the same performance to 3D human pose images as it does for a generic pose model.


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