Object Super-resolution via Low-Quality Lovate Recognition


Object Super-resolution via Low-Quality Lovate Recognition – We present a novel Deep Learning approach for semantic semantic segmentation in videos. Based on the knowledge learned, our approach first learns representations of the video frames and then learns to recognize them as well by performing a semi-supervised task with discriminative, multi-armed bandit algorithms. We train a fully convolutional neural network (CNN) to extract semantic segments from videos. Then, an action recognition module is combined to classify videos based on their semantic segmentation. In experiments, we demonstrate that our CNN classifier can significantly outperform an external end-to-end neural network (NN) and achieves state-of-the-art segmentation results.

This research aims to build a framework for multi-class data augmentation of deep convolutional neural networks (CNNs), using the multi-view and multi-level information. The idea is to combine the multi-view (high-level) information and its multi-level representations with a high-level (low-level) representation of the data. To achieve this goal, we propose learning a fully-connected CNN for multi-view CNNs and the use of multiple disjoint views and multiple connections in different order. The network learns a multi-view representation of the data. We evaluate the proposed method on multiple data augmentation benchmark datasets. Results show that our proposed framework is capable of outperforms state-of-the-art CNN augmentation techniques, without any additional expensive computation.

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Object Super-resolution via Low-Quality Lovate Recognition

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  • Multi-Context Reasoning for Question Answering

    Training Multi-class CNNs with Multiple Disconnected ConnectionsThis research aims to build a framework for multi-class data augmentation of deep convolutional neural networks (CNNs), using the multi-view and multi-level information. The idea is to combine the multi-view (high-level) information and its multi-level representations with a high-level (low-level) representation of the data. To achieve this goal, we propose learning a fully-connected CNN for multi-view CNNs and the use of multiple disjoint views and multiple connections in different order. The network learns a multi-view representation of the data. We evaluate the proposed method on multiple data augmentation benchmark datasets. Results show that our proposed framework is capable of outperforms state-of-the-art CNN augmentation techniques, without any additional expensive computation.


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