Jove
Visualize
Contact Us

Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

388
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
388
Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

791
Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
791
Parallel Processing01:20

Parallel Processing

373
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...
373

You might also read

Related Articles

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

Sort by
Same author

Automated monitoring of alcoholic fermentation: trends and challenges.

Journal of food science and technology·2026
See all related articles
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 Experiment Video

Updated: Oct 22, 2025

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention
05:36

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Published on: November 16, 2017

7.7K

Multimodal Emotion Recognition from Art Using Sequential Co-Attention.

Tsegaye Misikir Tashu1,2, Sakina Hajiyeva1, Tomas Horvath1,3

  • 1Department of Data Science and Engineering (T-Labs), Faculty of Informatics, Eötvös Loránd University, Pázmány Péter Sétány 1/C, 1117 Budapest, Hungary.

Journal of Imaging
|August 30, 2021
PubMed
Summary

This study introduces a multimodal emotion recognition system for art classification. The architecture uses attention mechanisms for improved feature extraction and modality fusion, enhancing emotion recognition accuracy.

Keywords:
artattentionemotion analysisemotionsmodality fusionmultimodal

More Related Videos

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

7.9K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.5K

Related Experiment Videos

Last Updated: Oct 22, 2025

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention
05:36

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Published on: November 16, 2017

7.7K
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

7.9K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.5K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Art History

Background:

  • Multimodal emotion recognition is crucial for understanding human-computer interaction and content analysis.
  • Previous methods often struggle with effectively fusing information from diverse data sources like images and text.
  • Art emotion classification presents unique challenges due to subjective interpretations and complex visual elements.

Purpose of the Study:

  • To develop and evaluate a novel multimodal emotion recognition architecture for classifying emotions evoked by artworks.
  • To enhance representation learning through feature-level attention (sequential co-attention) and modality attention (weighted modality fusion).
  • To demonstrate the system's utility in categorizing art, recommending pieces based on mood, and enabling content-based art retrieval.

Main Methods:

  • The proposed architecture integrates sequential co-attention for feature-level interaction and weighted modality fusion for combining different data sources.
  • Utilized three modalities for emotion recognition, focusing on extracting refined representations.
  • Experiments were conducted on the WikiArt emotion dataset.

Main Results:

  • The multimodal architecture demonstrated significant efficiency in emotion recognition tasks.
  • The integration of feature-level and modality attention improved the model's ability to learn informative representations.
  • The system proved effective in categorizing artworks by evoked emotions.

Conclusions:

  • The proposed multimodal emotion recognition architecture offers an effective approach for classifying emotions in art.
  • Attention mechanisms at both feature and modality levels are beneficial for enhancing representation learning.
  • The system's capabilities extend to practical applications such as art recommendation and content-based search.