Deep Learning for Scalable Object Detection and Recognition


Deep Learning for Scalable Object Detection and Recognition – We address the computational bottleneck of the recently proposed unsupervised learning algorithm (NSTA) for fine-grained classification of objects in videos. In this paper, we propose a novel unsupervised learning algorithm for fine-grained classification of objects in videos. Specifically, we leverage the non-stationary model of the video, which minimizes the divergence between the object’s pose and appearance; without this model, unsupervised learning can not be achieved in a principled manner, in spite of several theoretical and practical results. The unsupervised learning approach is based on extracting spatial relationships among the objects, which are learned from a set of pre-trained discriminant function (or images). We compare the performance of both unsupervised learning and a well-designed unsupervised learning algorithm on both fine-grained and fine-detailed object classification datasets. The performance improvement is demonstrated on the challenging task of object clustering from a collection of over-disturbed objects.

We propose a new framework for predicting and classifying the trajectories of two autonomous ships from the 3D spatial environment. At first it must estimate the direction of a ship’s course. The current method is not accurate and requires a computationally expensive strategy to calculate this decision. We present the approach of analyzing a game of Pareto-Landed Parachutes by using two novel data sets: the player’s journey in the Pareto Delta and the player’s journey in the North Atlantic. The player’s journey is assumed to be in a trajectory and the player’s trajectory is estimated using a simple simulation. Our approach can be performed by the player’s navigational and cognitive state and, due to it’s low-resolution, can be accurately computed by using a simple simulation. The goal of the approach is to provide a means for the player the ability to control the movement of the ship in the environment and thus improve navigation performance. In a series of experiments, we demonstrate that our approach has considerable potential to improve navigation in Pareto and indeed other environment scenarios.

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Deep Learning for Scalable Object Detection and Recognition

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  • A Hybrid Approach for 2D Face Retrieval

    Learning to Predict Grounding Direction: A Variational Nonparametric ApproachWe propose a new framework for predicting and classifying the trajectories of two autonomous ships from the 3D spatial environment. At first it must estimate the direction of a ship’s course. The current method is not accurate and requires a computationally expensive strategy to calculate this decision. We present the approach of analyzing a game of Pareto-Landed Parachutes by using two novel data sets: the player’s journey in the Pareto Delta and the player’s journey in the North Atlantic. The player’s journey is assumed to be in a trajectory and the player’s trajectory is estimated using a simple simulation. Our approach can be performed by the player’s navigational and cognitive state and, due to it’s low-resolution, can be accurately computed by using a simple simulation. The goal of the approach is to provide a means for the player the ability to control the movement of the ship in the environment and thus improve navigation performance. In a series of experiments, we demonstrate that our approach has considerable potential to improve navigation in Pareto and indeed other environment scenarios.


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