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Related Concept Videos

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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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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Exploring Emotional Stimuli Detection in Artworks: A Benchmark Dataset and Baselines Evaluation.

Tianwei Chen1, Noa Garcia1, Liangzhi Li2

  • 1Intelligence and Sensing Lab, Osaka University, Suita, Osaka 565-0871, Japan.

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|June 26, 2024
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Summary

Researchers developed a new task for detecting emotional stimuli in art, creating the APOLO dataset to benchmark AI emotion recognition. This work highlights challenges in art analysis and current AI limitations.

Keywords:
artwork analysisemotional stimuli detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Art Analysis

Background:

  • Understanding human emotion in art is complex due to diverse styles and subjective interpretations.
  • Current AI models face challenges in recognizing and localizing emotional triggers within artworks.

Purpose of the Study:

  • Introduce a novel task for detecting emotional stimuli in artworks.
  • Establish a benchmark for evaluating AI's capability in processing human emotion in art.
  • Create a dataset for quantifying emotional stimuli detection performance.

Main Methods:

  • Developed an "emotional stimuli detection task" focused on identifying regions that evoke emotions in art.
  • Constructed the APOLO dataset through crowd-sourced, pixel-level annotation of emotional stimuli.
  • Evaluated eight baseline methods, including a specialized model, on the dataset.

Main Results:

  • The APOLO dataset comprises 6,781 emotional stimuli across 4,718 artworks.
  • Experiments revealed the task's difficulty and the limitations of current AI techniques in art-emotion analysis.
  • Both qualitative and quantitative analyses demonstrated the challenges.

Conclusions:

  • The emotional stimuli detection task and APOLO dataset provide a valuable resource for advancing AI in art-emotion understanding.
  • Further research is needed to improve AI's ability to interpret subjective emotional content in diverse artistic styles.