Recognizing and Improving Textual Video by Interpreting Video Descriptions


Recognizing and Improving Textual Video by Interpreting Video Descriptions – This paper addresses the problem of extracting semantic features from textual data. We firstly present a new semantic segmentation method, namely Multistructure-Based Semantic Segmentation (MBSSE), that takes advantage of a semantic segmentation model to obtain better semantic features than the existing ones. Empirical evaluations on three datasets, including the MS-10 dataset, also demonstrate performance improvement over the existing ones. Furthermore, we compare MBSSE with a state-of-the-art semantic segmentation method, based on the Multistructure-based Temporal Segmentation.

In this manuscript we propose a novel approach to image-based semantic prediction which uses a new dataset with large-scale datasets with the ability to learn semantic information as inputs. We first learn the semantic information via a deep recurrent neural network, and we update this network using a learning-theory framework. We then apply our deep recurrent neural network to the semantic prediction task. We show that the learned semantic information and the learned visual features are complementary for a large variety of tasks with different semantic information. This suggests a significant improvement in semantic classification and semantic prediction over previous state-of-the-art visual recognition methods. Our neural network provides a simple approach to semantic prediction.

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Recognizing and Improving Textual Video by Interpreting Video Descriptions

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  • Frequency-based Feature Selection for Imbalanced Time-Series Data

    Sparse and Accurate Image Classification by Exploiting the Optimal EntropyIn this manuscript we propose a novel approach to image-based semantic prediction which uses a new dataset with large-scale datasets with the ability to learn semantic information as inputs. We first learn the semantic information via a deep recurrent neural network, and we update this network using a learning-theory framework. We then apply our deep recurrent neural network to the semantic prediction task. We show that the learned semantic information and the learned visual features are complementary for a large variety of tasks with different semantic information. This suggests a significant improvement in semantic classification and semantic prediction over previous state-of-the-art visual recognition methods. Our neural network provides a simple approach to semantic prediction.


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