A Framework for Identifying and Mining Object Specific Instances from Compressed Video Frames

A Framework for Identifying and Mining Object Specific Instances from Compressed Video Frames – This paper addresses the problem of using a video capture system to capture a 3D shape of an object in real-time. Using video frames from the same object, there is a large amount of information about the object and its physical motion. We propose a video recognition framework, in which it is possible to directly extract the objects location and the motion within video frames, through the use of a deep network, to make an efficient reconstruction of the video frames. In addition, we propose an iterative method for the recognition of object location, motion and object oriented parts of video frames on the basis of the 3D features. We validate the performance of our approach by utilizing object-oriented parts and pose of objects.

This paper addresses the problem of multi-dimensional Gaussian network (GAN) model generation from sparse data under some conditions. Recently, deep learning models have been proposed to learn state-of-the-art visual features from data sets or datasets, but the problem is still poorly understood. In this paper, we develop a novel deep learning architecture for feature generation from unsupervised sparse data, which outperforms state-of-the-art GAN methods by a large margin. We propose an efficient and generalization-free learning strategy that learns feature representations for both supervised and unsupervised data sets. We further improve this strategy by training the residual model on the data, which, in turn, provides a new discriminant analysis for the learned features. Experiments on the ImageNet dataset show that using our approach improves the performance of our unsupervised GAN model for several benchmark classification tasks, including image classification and text classification.

Interpretability in Machine Learning

Learning Data Representations for Video Classification with Convolutional Neural Networks

A Framework for Identifying and Mining Object Specific Instances from Compressed Video Frames

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  • Deep learning for the classification of emotionally charged events

    Stochastic gradient descent using sparse regularizationThis paper addresses the problem of multi-dimensional Gaussian network (GAN) model generation from sparse data under some conditions. Recently, deep learning models have been proposed to learn state-of-the-art visual features from data sets or datasets, but the problem is still poorly understood. In this paper, we develop a novel deep learning architecture for feature generation from unsupervised sparse data, which outperforms state-of-the-art GAN methods by a large margin. We propose an efficient and generalization-free learning strategy that learns feature representations for both supervised and unsupervised data sets. We further improve this strategy by training the residual model on the data, which, in turn, provides a new discriminant analysis for the learned features. Experiments on the ImageNet dataset show that using our approach improves the performance of our unsupervised GAN model for several benchmark classification tasks, including image classification and text classification.


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