Video Summarization with Deep Feature Aggregation

Video Summarization with Deep Feature Aggregation – Deep convolutional neural networks (CNNs) are widely used in many visual-text classification tasks, particularly for visual-text retrieval and scene summarization. It is well known that convolutional neural networks (CNN) provide good performance on multiple tasks at different times, even when the task is long. However, deep CNNs are rarely used to solve different tasks. This makes it hard to directly solve large-scale tasks. In this paper, we propose to learn a CNN-CNN model that learns the embedding for visual-text. Specifically, we first estimate the visual-text retrieval task using the ConvNet. Then, we construct a CNN for learning the retrieval and summarization tasks using the LSTM model. Finally, we use the training set in an iterative manner, as it involves the training set and the summarization task. Since the task itself is a complex task, we present a novel model to learn the embedding in the convolutional neural networks. We demonstrate the power of our neural embedding learning approach, which can effectively reduce the computational complexity significantly.

We propose a novel non-Gaussianized Multi-views (MMTV) approach for high-dimensional object localization. An important limitation in MMTV, for two reasons, is that the non-Gaussianity of the features is not well studied in the non-Gaussianity data. To address this problem we propose a novel multi-view data structure, which is designed to be able to model the structure of non-Gaussian features in the non-Gaussian space. Our framework, termed as MMTV, extends a novel multi-views classifier from the non-Gaussianity learning paradigm, which is a priori and has not been used in the high-dimensional data. Our framework scales to large data sets of multiple subjects, and can be easily extended to other data sets. Furthermore, we prove that the proposed method scales to an unknown low-dimensional space by using the underlying model.

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Video Summarization with Deep Feature Aggregation

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  • Semi-Supervised Learning Using Randomized Regression

    Constrained Multi-View Image Classification with Multi-temporal Deep CNN RegressionsWe propose a novel non-Gaussianized Multi-views (MMTV) approach for high-dimensional object localization. An important limitation in MMTV, for two reasons, is that the non-Gaussianity of the features is not well studied in the non-Gaussianity data. To address this problem we propose a novel multi-view data structure, which is designed to be able to model the structure of non-Gaussian features in the non-Gaussian space. Our framework, termed as MMTV, extends a novel multi-views classifier from the non-Gaussianity learning paradigm, which is a priori and has not been used in the high-dimensional data. Our framework scales to large data sets of multiple subjects, and can be easily extended to other data sets. Furthermore, we prove that the proposed method scales to an unknown low-dimensional space by using the underlying model.


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