On the View-Invariant Representation Learning of High-Order Images


On the View-Invariant Representation Learning of High-Order Images – This paper proposes a novel approach for learning a global feature of a dataset consisting of multiple categories, whose labels are composed by a single category matrix and one subcategory matrix, to automatically form a global feature vector. The resulting feature can be learned by learning a new and complex representation of the data without modifying the existing classification scheme. In this paper, an intermediate representation of this feature matrix is derived using a recurrent neural network (RNN). Experimental evaluation on several synthetic datasets and in vivo experiments on real data have illustrated that the proposed approach outperforms baselines and a robust classification approach is proposed.

We present an application of deep learning techniques for automatic gesture recognition from facial expressions. Based on convolutional neural networks (CNNs), we propose a novel deep learning model for object detection which addresses the problem of using object detection to detect occluded objects. We leverage a Convolutional Neural Network (CNN) to learn the object features in a CNN, and then train the object detector to find the occluded objects. To test our model on real-world applications, we perform this challenge against three different datasets based on the human test set. The test sets are of different kinds and in each case, object recognition was achieved at different difficulty levels compared to the other hand. Furthermore, we demonstrate that our method is capable of detecting objects that are more similar to the human than to similar objects.

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On the View-Invariant Representation Learning of High-Order Images

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  • The Power of Zero

    DeepKSPD: Learning to detect unusual motion patterns in videosWe present an application of deep learning techniques for automatic gesture recognition from facial expressions. Based on convolutional neural networks (CNNs), we propose a novel deep learning model for object detection which addresses the problem of using object detection to detect occluded objects. We leverage a Convolutional Neural Network (CNN) to learn the object features in a CNN, and then train the object detector to find the occluded objects. To test our model on real-world applications, we perform this challenge against three different datasets based on the human test set. The test sets are of different kinds and in each case, object recognition was achieved at different difficulty levels compared to the other hand. Furthermore, we demonstrate that our method is capable of detecting objects that are more similar to the human than to similar objects.


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