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Related Concept Videos

The Ideal Transformer01:26

The Ideal Transformer

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Types Of Transformers01:16

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Related Experiment Video

Updated: Apr 21, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

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Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition.

Jian Sun1,2, Mohammad Mahoor1

  • 1Department of Computer Science, University of Denver, 2155 E Wesley Ave, Denver, CO 80210, USA.

Neural Computing & Applications
|April 20, 2026
PubMed
Summary

Integrating video quality assessment improves video classification accuracy, especially for medical applications like Mild Cognitive Impairment detection. This new method enhances classification performance on blurred videos.

Keywords:
Contrastive learningData imbalanceNR-VQASelf-supervised learningViViTVideo classification

Related Experiment Videos

Last Updated: Apr 21, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Medical Informatics

Background:

  • Video quality is crucial for accurate video classification.
  • Blurred videos hinder the classification of conditions like Mild Cognitive Impairment.
  • Video Quality Assessment (VQA) can potentially improve video classification.

Purpose of the Study:

  • To propose a novel method, SSL-V3, combining Self-Supervised Learning-based Video Vision Transformer with No-reference VQA for enhanced video classification.
  • To address the challenge of limited labeled data in VQA datasets.

Main Methods:

  • Developed SSL-V3, integrating VQA into video classification using a Combined-SSL mechanism.
  • Used video quality scores to directly tune feature maps for classification.
  • Employed supervised classification tasks to optimize VQA parameters, linking both tasks.

Main Results:

  • SSL-V3 demonstrated robust performance on two datasets.
  • Achieved 94.87% accuracy in classifying interview videos within the I-CONECT healthcare dataset.
  • Verified the effectiveness of the proposed method in improving video classification.

Conclusions:

  • The proposed SSL-V3 method effectively enhances video classification by incorporating video quality assessment.
  • The Combined-SSL mechanism successfully links VQA and classification, overcoming data limitations.
  • SSL-V3 shows significant promise for applications in healthcare and beyond.