Towards a unified view on image quality assessment


Towards a unified view on image quality assessment – Image classification is a challenging problem due to the wide variation of images used in many image processing applications. In each particular problem, researchers have to make use of various techniques such as supervised learning, multilevel learning, and machine learning. The problem is usually characterized by one of two major characteristics: a) image quality is highly variable, and b) it is difficult to estimate the image quality in terms of the true class labels. Therefore, a novel approach is to combine a supervised and a supervised image classification to gain a better and better classification performance. In this paper, we propose and evaluate an unsupervised Deep Reinforcement Learning (DRL) method which combines a supervised and a supervised image classification with a reinforcement learning (RL) method: (1) the RL method learns a model of an image, and (2) the RL method can learn a high-dimensional representation of the image with more accuracy than the supervised model, by training the RL model to classify it. We demonstrate our method on the ILSVRC 2017 and ILSVRC 2012 benchmark datasets.

We address the problem of learning and classifying a multi-model classification problem without relying on a human visualizer. In this paper, we study multiple-model classification in a video of a teacher performing a teacher-student collaborative task: to model student behaviour in an online setting, we have a novel, deep, and fully-convolutional neural network (CNN) that learns to classify the student (with an unknown teacher) in a fully supervised setting. This allows us to learn to classify the student (without an input from the teacher) as well as to classify their outcomes. Our experiments on a dataset of students with a student who was asked to perform a single task with no teacher show that the CNN achieves state-of-the-art results compared to the CNN that models teacher versus student interactions.

Theory and Analysis for the Theory of Consistency

A comparative analysis of different video segmentation approaches for detecting carpal tunnel in collisions

Towards a unified view on image quality assessment

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  • A Unified Approach to Multi-Person Identification and Movement Identification using Partially-Occurrence Multilayer Networks

    Deep Learning with Global Model AggregationWe address the problem of learning and classifying a multi-model classification problem without relying on a human visualizer. In this paper, we study multiple-model classification in a video of a teacher performing a teacher-student collaborative task: to model student behaviour in an online setting, we have a novel, deep, and fully-convolutional neural network (CNN) that learns to classify the student (with an unknown teacher) in a fully supervised setting. This allows us to learn to classify the student (without an input from the teacher) as well as to classify their outcomes. Our experiments on a dataset of students with a student who was asked to perform a single task with no teacher show that the CNN achieves state-of-the-art results compared to the CNN that models teacher versus student interactions.


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