A New Spectral Feature Selection Method for Object Detection in Unstructured Contexts


A New Spectral Feature Selection Method for Object Detection in Unstructured Contexts – Objects are often used in various scientific disciplines and have been used for various applications. In this paper, we propose a new approach to object detection from visual data. We propose a novel visual-based object detection method, which is based on a hierarchical convolutional neural network, which achieves a similar performance as the state-of-the-art object detection. However, we also propose a simple and efficient method which has a fast convergence rate, which is much faster than current state-of-the-art object detection methods. We demonstrate the proposed model for using visual data in the learning of spatial semantic concepts, which is the main reason why it is capable to solve various tasks in object detection such as classification and recognition.

Video synthesis has been proposed as a technique to improve the performance of a video synthesis task. In this paper, we investigate the effect of several recent video synthesis methods on video synthesis tasks. We study two different video synthesis methods using an adversarial framework to generate video frames with different levels of classification. First, we propose an unsupervised classifier called VideoNet-AUC to generate low-level classification frames. In addition, we propose a method to predict visual attributes such as color, texture, and size. We demonstrate the effectiveness of the proposed method on three publicly available datasets and compare the results. The proposed method compared favorably with the unsupervised methods on multiple video synthesis tasks.

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A New Spectral Feature Selection Method for Object Detection in Unstructured Contexts

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  • Evolving Minimax Functions via Stochastic Convergence Theory

    Unsupervised Video Summarization via Deep LearningVideo synthesis has been proposed as a technique to improve the performance of a video synthesis task. In this paper, we investigate the effect of several recent video synthesis methods on video synthesis tasks. We study two different video synthesis methods using an adversarial framework to generate video frames with different levels of classification. First, we propose an unsupervised classifier called VideoNet-AUC to generate low-level classification frames. In addition, we propose a method to predict visual attributes such as color, texture, and size. We demonstrate the effectiveness of the proposed method on three publicly available datasets and compare the results. The proposed method compared favorably with the unsupervised methods on multiple video synthesis tasks.


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