Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Visual System01:26

Visual System

639
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...
639
Vision01:24

Vision

54.6K
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.
54.6K
Parallel Processing01:20

Parallel Processing

196
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
196
Neural Circuits01:25

Neural Circuits

1.4K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Development of an In-Pipe Inspection Robot for Large-Diameter Water Pipes.

Sensors (Basel, Switzerland)·2024
Same author

Foot contact forces can be used to personalize a wearable robot during human walking.

Scientific reports·2022
Same author

Necrotizing Soft-Tissue Infections: A Retrospective Review of Predictive Factors for Limb Loss.

Clinics in orthopedic surgery·2022
Same author

Morphological and Chemical Evaluations of Leaf Surface on Particulate Matter2.5 (PM2.5) Removal in a Botanical Plant-Based Biofilter System.

Plants (Basel, Switzerland)·2021
Same author

Visual question answering based on local-scene-aware referring expression generation.

Neural networks : the official journal of the International Neural Network Society·2021
Same author

Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning-Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation.

Journal of medical Internet research·2020

Related Experiment Video

Updated: Aug 10, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.4K

Neural Architecture Search Survey: A Computer Vision Perspective.

Jeon-Seong Kang1, JinKyu Kang2, Jung-Jun Kim1

  • 1AI Robotics R&D Division, Korea Institute of Robotics & Technology Convergence, Seoul 06372, Republic of Korea.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

Automated neural architecture search (NAS) is gaining attention for designing deep learning models. This study uniquely reviews NAS applications from a computer vision viewpoint, categorizing tasks and analyzing trends.

Keywords:
artificial intelligence (AI)automated machine learning (Auto-ML)computer vision (CV)convolutional neural network (CNN)deep learning (DL)neural architecture search (NAS)

More Related Videos

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients
07:43

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

7.8K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Related Experiment Videos

Last Updated: Aug 10, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.4K
Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients
07:43

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

7.8K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning (DL) models are effective across various applications like image, speech, and text recognition.
  • Designing effective neural architectures requires expertise and extensive trial-and-error.
  • Automated Neural Architecture Search (NAS) methods are emerging as a solution to streamline architecture design.

Purpose of the Study:

  • To summarize basic concepts of NAS.
  • To provide an overview of recent NAS applications.
  • To analyze NAS trends from a computer vision perspective, a novel approach compared to previous hardware or search strategy focused surveys.

Main Methods:

  • Categorization of computer vision areas by task.
  • Detailed analysis of recent NAS studies within each task category.
  • Review of existing literature on NAS, focusing on its application in computer vision.

Main Results:

  • Identified key trends in NAS applications across different computer vision tasks.
  • Highlighted the novelty of a computer vision-centric perspective in NAS research.
  • Provided a comprehensive overview of NAS advancements relevant to visual recognition and related fields.

Conclusions:

  • NAS is crucial for advancing DL model design, particularly in computer vision.
  • This study offers a unique perspective by focusing on computer vision tasks.
  • Further research can build upon this analysis to develop more efficient and specialized NAS methods for visual AI.