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

Stereotype Content Model02:16

Stereotype Content Model

15.3K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.3K
Impression Management Techniques IV: Altercasting01:14

Impression Management Techniques IV: Altercasting

157
Altercasting is a strategic communication technique in which an individual imposes a specific identity or social role onto another person to influence their behavior and shape the interaction. By presuming a role—such as “responsible leader” or “patient person”—altercasting encourages the target to conform to that identity, often aligning their behavior with the expectations associated with the role. The power of this tactic lies in its subtlety; once a role...
157
Design Example01:23

Design Example

516
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
516
Impression Management Techniques I: Managing Appearances01:29

Impression Management Techniques I: Managing Appearances

187
Appearance is a multidimensional aspect of self-presentation that encompasses observable attributes such as clothing, grooming, speech, and nonverbal behavior. These elements are often strategically managed to align with socially constructed expectations in different settings. For instance, individuals tailor their appearance during job interviews, social gatherings, or athletic events to meet the perceived norms of those environments.Contextual Adaptation and Social SignalsThe research...
187
Divergence and Curl01:15

Divergence and Curl

3.1K
The divergence of a vector field at a point is the net outward flow of the flux out of a small volume through a closed surface enclosing the volume, as the volume tends to zero. More practically, divergence measures how much a vector field spreads out or diverges from a given point. For an outgoing flux, conventionally, the divergence is positive. The diverging point is often called the "source" of the field. Meanwhile, the negative divergence of a vector field at a point means that the vector...
3.1K
Position-effect Variegation02:32

Position-effect Variegation

7.0K
In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
7.0K

You might also read

Related Articles

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

Sort by
Same author

Refining 3D Human Texture Estimation From a Single Image.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Progressive Learning of 3D Reconstruction Network From 2D GAN Data.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Fine Detailed Texture Learning for 3D Meshes With Generative Models.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Benchmarking the Robustness of Instance Segmentation Models.

IEEE transactions on neural networks and learning systems·2023
Same author

Image-to-Image Translation With Disentangled Latent Vectors for Face Editing.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Partial Convolution for Padding, Inpainting, and Image Synthesis.

IEEE transactions on pattern analysis and machine intelligence·2022
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

IEEE transactions on neural networks and learning systems·2026
Same journal

Stability of Time-Varying Impulsive Systems With State-Dependent Delay and Its Application in Complex Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Adaptive Learning Control of Uncertain Systems via Weight and Intrinsic Plasticity-Based Neural Networks.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.5K

Diverse Semantic Image Editing With Style Codes.

Hakan Sivuk, Aysegul Dundar

    IEEE Transactions on Neural Networks and Learning Systems
    |December 12, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new framework for semantic image editing, improving how new objects are integrated into images. The method ensures style consistency and better boundary blending for more realistic and diverse results.

    More Related Videos

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.8K
    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

    12.2K

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    9.5K
    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.8K
    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

    12.2K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Semantic image editing requires harmonizing inpainted regions with context and semantic constraints.
    • Existing methods struggle with style inference for new objects and seamless boundary generation.
    • Prior approaches often encode information solely from erased regions, limiting context-aware generation.

    Purpose of the Study:

    • To propose a novel framework for semantic image editing that addresses limitations in style consistency and boundary blending.
    • To enhance the generation of diverse and contextually appropriate images with new objects.
    • To improve the state-of-the-art in conditional image generation and semantic image editing.

    Main Methods:

    • Developed a framework encoding visible and partially visible objects.
    • Introduced a novel mechanism for style encoding consistency.
    • Implemented a method for seamless boundary generation between edited and original image regions.

    Main Results:

    • Significantly improved over state-of-the-art methods in quantitative evaluations.
    • Achieved better performance in semantic image editing tasks.
    • Demonstrated the ability to produce diverse and high-quality image generations.

    Conclusions:

    • The proposed framework offers a significant advancement in semantic image editing.
    • The method successfully addresses challenges in style consistency and boundary blending.
    • Future work includes releasing a demo and code for broader accessibility.