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Related Concept Videos

Vision01:24

Vision

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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

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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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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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.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
364
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

401
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
401
Visual System01:26

Visual System

557
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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Comprehensive comparison between vision transformers and convolutional neural networks for face recognition tasks.

Marcos Rodrigo1, Carlos Cuevas2, Narciso García2

  • 1Grupo de Tratamiento de Imágenes (GTI), Information Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid (UPM), Madrid, 28040, Spain. marcos.rodrigo@upm.es.

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Summary
This summary is machine-generated.

Vision Transformers outperform Convolutional Neural Networks for face recognition tasks, showing superior accuracy and robustness against occlusions and distance. These models offer a more efficient solution with faster inference and smaller memory footprints.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Face recognition is a critical task in computer vision.
  • Convolutional Neural Networks (CNNs) have been dominant, but Vision Transformers (ViTs) are emerging.
  • Evaluating ViTs against established CNNs on diverse, challenging datasets is necessary.

Purpose of the Study:

  • To conduct a comprehensive comparison of Vision Transformers and Convolutional Neural Networks for face recognition.
  • To evaluate model performance across various datasets with occlusions and distance variations.
  • To analyze training, evaluation, and hardware/software configurations.

Main Methods:

  • Comparative analysis of six state-of-the-art models: EfficientNet, Inception, MobileNet, ResNet, VGG, and Vision Transformers.
  • Extensive experiments on five diverse face recognition datasets (LFw, RWF, SCF, UPM-GTI-Face, VGG Face 2).
  • In-depth examination of experimental results, including training, evaluation, and system configurations.

Main Results:

  • Vision Transformers demonstrated superior accuracy and robustness compared to CNNs for face identification and verification.
  • ViTs showed significant improvements in handling face occlusions (masks, glasses) and varying distances.
  • ViTs achieved competitive inference speeds and required less memory than comparable CNN models.

Conclusions:

  • Vision Transformers represent a more efficient and effective solution for face recognition tasks than traditional CNNs.
  • ViTs offer enhanced performance, particularly in challenging real-world scenarios.
  • The study provides valuable insights for future research and development in face recognition technology.