Sparse Representation based Object Detection with Hierarchy Preserving Homology


Sparse Representation based Object Detection with Hierarchy Preserving Homology – Hierarchical classification models are used to identify objects based on structure similarity or similarity metrics. Hierarchical classification models are useful for many natural and natural-looking tasks such as image classification, object recognition and image categorization. Most existing classification methods have a hierarchical representation of object instances but little is known about object types such as shape, shape-based and shape-based pose. In this paper, we propose a new hierarchical classification model, Hierarchical Classification-Hierarchical Classification (ICCD) which has a hierarchical model that represents each instance in its hierarchy according to its shape and pose. The proposed hierarchical classification model achieves classification accuracy with respect to the previous state-of-the-art classification methods with high confidence.

Many recent methods for deep reinforcement learning (RL) rely on the use of multi-dimensional convolutional neural networks. This paper investigates the use of multi-dimensional convolutional neural networks (MDS-NNs) for non-linear reinforcement learning (NRL) tasks. We present a novel approach that employs convolutional networks for nonlinear RL tasks, which, by a neural network’s own, leads to efficient policy learning that avoids the need for costly re-training. We show that a nonlinear RL task may be more suited to a multi-dimensional MDS-NN, as it has a fully-connected network with an input manifold and a policy space. Moreover, we show that a nonlinear RL task (e.g., a simple image navigation task) may be more attractive to a multi-dimensional MDS-NN than a simple image detection task. Moreover, we obtain efficient policies for a simple RL task as a result of our approach.

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Sparse Representation based Object Detection with Hierarchy Preserving Homology

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  • Single-Shot Recognition with Deep Priors

    Multi-view Graph Convolutional Neural NetworkMany recent methods for deep reinforcement learning (RL) rely on the use of multi-dimensional convolutional neural networks. This paper investigates the use of multi-dimensional convolutional neural networks (MDS-NNs) for non-linear reinforcement learning (NRL) tasks. We present a novel approach that employs convolutional networks for nonlinear RL tasks, which, by a neural network’s own, leads to efficient policy learning that avoids the need for costly re-training. We show that a nonlinear RL task may be more suited to a multi-dimensional MDS-NN, as it has a fully-connected network with an input manifold and a policy space. Moreover, we show that a nonlinear RL task (e.g., a simple image navigation task) may be more attractive to a multi-dimensional MDS-NN than a simple image detection task. Moreover, we obtain efficient policies for a simple RL task as a result of our approach.


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