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.

This paper presents a novel technique for the generation of a 3D shape using a novel spectral feature descriptor (similar to Bayesian LSTMs) from a dataset of 3D landmarks with only 3D point locations. The first feature descriptor is trained with a pre-trained convnet and the second one by sampling the data from the pre-trained CNN, and the final feature descriptor is extracted by using a neural network-based Convolutional Neural Network (CNN). We train CNN and provide a synthetic data set of 3D landmarks with only 3D points, which will allow us to learn the feature descriptor from a new dataset. Our methods also provide a new dataset of 3D landmarks with 3D points, which is a more challenging task due to the high dimension and low quality of the landmarks. We collected training data from a single location dataset, which was used to evaluate our CNN network using 2D hand-drawn annotations. Our experiments on benchmark datasets using state-of-the-art CNNs lead to improved state of the art performance.

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Recognizing and Improving Textual Video by Interpreting Video Descriptions

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  • Learning from Past Profiles

    Nonparametric Nearest Neighbor Clustering with Kernel RegressionThis paper presents a novel technique for the generation of a 3D shape using a novel spectral feature descriptor (similar to Bayesian LSTMs) from a dataset of 3D landmarks with only 3D point locations. The first feature descriptor is trained with a pre-trained convnet and the second one by sampling the data from the pre-trained CNN, and the final feature descriptor is extracted by using a neural network-based Convolutional Neural Network (CNN). We train CNN and provide a synthetic data set of 3D landmarks with only 3D points, which will allow us to learn the feature descriptor from a new dataset. Our methods also provide a new dataset of 3D landmarks with 3D points, which is a more challenging task due to the high dimension and low quality of the landmarks. We collected training data from a single location dataset, which was used to evaluate our CNN network using 2D hand-drawn annotations. Our experiments on benchmark datasets using state-of-the-art CNNs lead to improved state of the art performance.


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