Deep Learning for Human Action Detection: Dataset and Experiments – In many applications, the task of detection of an object is a complex and challenging task. We propose a new type of object-class image named feature-based object classification by integrating spatial information of the object with the object class information. To overcome the limitations posed by feature learning methods and by human intervention, we present a novel spatial object classifier called MEC-classifier. It is trained as a multi-level object detector, whereas MEC-classifier employs a unified feature hierarchy to identify the key feature regions that contribute to the object classification success. We demonstrate the effectiveness of MEC-classifier by a dataset of 10 million object instances from the Amazon Amazon Web Services (AWS) dataset.

In this paper, we present a new method that automatically selects face images for each user based on prior knowledge. Specifically, we use Google Now, an image search service, to detect facial features of users from their videos and image sequences. We have developed a deep neural network that automatically learns how to extract such features using a deep learning framework from images. To this end, we evaluate the new technique by a dataset of human face images under human face datasets, demonstrating that it significantly outperformed the human model.

A Discriminative Model for Relation Discovery

Lifelong Learning for Answerability Education

Deep Learning for Human Action Detection: Dataset and Experiments

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The Evolution of Lexical Variation: Does Language Matter?

Evaluation of Feature-based Face Recognition Methods: A Preliminary ReportIn this paper, we present a new method that automatically selects face images for each user based on prior knowledge. Specifically, we use Google Now, an image search service, to detect facial features of users from their videos and image sequences. We have developed a deep neural network that automatically learns how to extract such features using a deep learning framework from images. To this end, we evaluate the new technique by a dataset of human face images under human face datasets, demonstrating that it significantly outperformed the human model.