A Unified Approach for Online Video Quality Control using Deep Neural Network Technique

A Unified Approach for Online Video Quality Control using Deep Neural Network Technique – We propose a novel framework for automatic video quality control, which includes an active learning setting, which can be used to learn the properties of the video by predicting the video quality parameters for each individual instance. This framework is built upon a novel training scenario where the training data is generated by an agent and the control system learns to optimize the video quality parameters, using neural networks. We demonstrate that our framework leads to effective learning of video to improve the quality of the video. We discuss various methods and show how our framework can be used as a generic framework for video quality control and an efficient user-friendly software.

We present a novel method to model the human gaze through a multi-spectral image of an object. Using deep neural networks, the network learns to learn a map and map directions for a given image from a few image features. The method can be used to extract objects from the image, or it can be used to infer features from a single image. We show how to learn the mapping, with deep learning that learns to model the image, and learning to infer the features (i.e. the map direction). The proposed algorithm is evaluated on four different benchmark datasets and shows promising results.

On top of existing computational methods for adaptive selection

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A Unified Approach for Online Video Quality Control using Deep Neural Network Technique

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  • Improving Human-Annotation Vocabulary with Small Units: Towards Large-Evaluation Deep Reinforcement Learning

    Semi-Supervised Learning for Image-TemplatesWe present a novel method to model the human gaze through a multi-spectral image of an object. Using deep neural networks, the network learns to learn a map and map directions for a given image from a few image features. The method can be used to extract objects from the image, or it can be used to infer features from a single image. We show how to learn the mapping, with deep learning that learns to model the image, and learning to infer the features (i.e. the map direction). The proposed algorithm is evaluated on four different benchmark datasets and shows promising results.


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