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

Fixing Double-strand Breaks02:04

Fixing Double-strand Breaks

3.1K
3.1K
Focusing of Light in the Eye01:16

Focusing of Light in the Eye

2.9K
Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...
2.9K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.2K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.2K
Proofreading01:43

Proofreading

54.2K
Overview
54.2K
Tip-of-the-Tongue Phenomenon01:10

Tip-of-the-Tongue Phenomenon

167
The tip-of-the-tongue (TOT) phenomenon is a cognitive experience characterized by a temporary inability to retrieve specific information from memory despite having a strong feeling of knowing the information. Although individuals cannot access the target word or detail, they frequently recall related elements, such as its initial letter, syllable count, or context. This partial retrieval often causes frustration, as one might recognize a familiar face or know that a name starts with a specific...
167
Additional Subnuclear Structures02:10

Additional Subnuclear Structures

2.1K
2.1K

You might also read

Related Articles

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

Sort by
Same author

Divergent pathogenic mechanisms of influenza A and influenza B viruses.

Archives of microbiology·2026
Same author

AARS2-mediated lactylation of ULK1 promotes autophagy-dependent progression of clear cell renal cell carcinoma.

Autophagy·2026
Same author

Development and external validation of a multivariable nomogram for predicting severe immune checkpoint inhibitor-associated myocarditis in advanced lung cancer.

Translational lung cancer research·2026
Same author

Performance comparison of large language models for medication counseling in people living with HIV.

Frontiers in public health·2026
Same author

Clinical Characteristics and Survival Outcomes in a Cohort of Pediatric Rhabdomyosarcoma Patients: The Impact of Risk-Adapted Therapy.

Cancers·2026
Same author

Real-Time Transient Voltage and Frequency Sensing Strategy for Resilience Enhancement of PV-Storage Systems in Weak Grids.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jul 17, 2025

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

6.2K

SPTS v2: Single-Point Scene Text Spotting.

Yuliang Liu, Jiaxin Zhang, Dezhi Peng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 5, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Our new framework, SPTS v2, enables high-performance scene text spotting using only single-point annotations. This approach significantly reduces annotation costs and achieves 19x faster inference than prior methods.

    More Related Videos

    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
    07:36

    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

    Published on: November 30, 2018

    15.8K
    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
    05:51

    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

    Published on: May 15, 2016

    9.1K

    Related Experiment Videos

    Last Updated: Jul 17, 2025

    Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
    05:54

    Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

    Published on: October 18, 2018

    6.2K
    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
    07:36

    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

    Published on: November 30, 2018

    15.8K
    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
    05:51

    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

    Published on: May 15, 2016

    9.1K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • End-to-end scene text spotting integrates text detection and recognition.
    • Traditional methods require expensive manual annotations like bounding boxes or polygons.
    • Single-point annotation offers a more cost-effective alternative.

    Purpose of the Study:

    • To develop a novel framework, SPTS v2, for efficient scene text spotting using single-point annotations.
    • To leverage the Transformer architecture with specialized decoders for improved performance and speed.
    • To demonstrate the viability and advantages of single-point representation in scene text spotting.

    Main Methods:

    • SPTS v2 utilizes an auto-regressive Transformer with an Instance Assignment Decoder (IAD) for sequential center point prediction.
    • A Parallel Recognition Decoder (PRD) handles text recognition concurrently, reducing sequence length requirements.
    • Both decoders share parameters and interact through an efficient information transmission process.

    Main Results:

    • SPTS v2 achieves state-of-the-art performance on benchmark datasets using single-point annotations.
    • The framework demonstrates superior efficiency with fewer parameters and a 19x faster inference speed compared to previous methods.
    • Experimental results indicate a potential preference for single-point representation in scene text spotting.

    Conclusions:

    • SPTS v2 offers a significant advancement in scene text spotting by enabling high performance with minimal annotation effort.
    • The proposed method presents a more practical and scalable solution for real-world scene text spotting applications.
    • This work opens new avenues for scene text spotting beyond current paradigms, emphasizing the efficacy of single-point annotations.