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Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

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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...
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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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.
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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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Related Experiment Video

Updated: Aug 15, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Multimodal interaction enhanced representation learning for video emotion recognition.

Xiaohan Xia1, Yong Zhao1, Dongmei Jiang1,2

  • 1Shaanxi Key Laboratory on Speech and Image Information Processing, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China.

Frontiers in Neuroscience
|January 5, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for video emotion recognition, enhancing audio and visual data with text semantics. The proposed method significantly outperforms existing models in accurately identifying emotions.

Keywords:
cross-attentioncross-modal interactionemotion recognitionrepresentation learningsemantic enhancement

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Video emotion recognition utilizes audio, visual, and text data.
  • Current methods often overlook text's semantic role in emotion expression.
  • Facial expressions and speech provide fine-grained emotional dynamics regulated by semantics.

Purpose of the Study:

  • To develop a multimodal framework for enhanced emotion recognition from face video.
  • To leverage text semantics to guide audio-visual feature extraction.
  • To improve emotion recognition accuracy by modeling cross-modal interactions.

Main Methods:

  • A semantic enhancement module guides audio/visual encoders with text information.
  • A multimodal bottleneck Transformer models dynamic cross-modal interactions.
  • The framework integrates semantic enhancement and cross-modal interaction for representation learning.

Main Results:

  • The proposed method demonstrates superior performance on benchmark emotion databases.
  • Semantic-enhanced audio and visual features lead to improved emotion recognition.
  • Outperforms state-of-the-art models in multimodal emotion recognition.

Conclusions:

  • Integrating text semantics significantly enhances audio-visual emotion recognition.
  • Modeling cross-modal dynamics is crucial for accurate emotion inference.
  • The proposed framework offers a novel approach to multimodal emotion recognition.