Constrained Multi-View Image Classification with Multi-temporal Deep CNN Regressions


Constrained Multi-View Image Classification with Multi-temporal Deep CNN Regressions – In this paper, we propose to model the input and output characteristics of the images by utilizing a combination of 3D and depth features in a manner to extract meaningful semantic information from the image. Unlike conventional 2D CNN based supervised learning, we propose a novel 3D segmentation and 3D convolutional neural network based approach for the 3D segmentation task. This new CNN architecture is able to be adapted to handle the different aspects needed by 3D CNNs, i.e., the feature representation and the 3D depth information. We evaluated our method on two datasets, one with RGB-D data and one without RGB-D data, and compared our methods on both datasets. Experimental results show that the proposed approach significantly outperforms the state-of-the-art CNN methods and also achieves state-of-the-art results on both datasets.

Despite efforts to develop the deep learning based video processing (VCR) system for movie data, the quality of the results that video content provides is still limited due to variations in the scene content. To overcome this challenge, we propose a new VSCRE algorithm for extracting the scene information that best serves the task of VCR classification, and demonstrate the performance of the proposed algorithm by using the image dataset from the CINDA 2017. The proposed technique leverages visual, semantic and phonetic information to obtain a fully convolutional video representation of the VCR.

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Constrained Multi-View Image Classification with Multi-temporal Deep CNN Regressions

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  • A Deep Neural Network Based Multiscale Transformer Network for Multi-Label Speech Recognition

    Evaluating a Convolutional Deep Architecture for Video PredictionDespite efforts to develop the deep learning based video processing (VCR) system for movie data, the quality of the results that video content provides is still limited due to variations in the scene content. To overcome this challenge, we propose a new VSCRE algorithm for extracting the scene information that best serves the task of VCR classification, and demonstrate the performance of the proposed algorithm by using the image dataset from the CINDA 2017. The proposed technique leverages visual, semantic and phonetic information to obtain a fully convolutional video representation of the VCR.


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