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Cognitive Relevance Transform for Population Re-Targeting.

Gregor Koporec1, Andrej Košir2, Aleš Leonardis3

  • 1Gorenje, d. o. o., Partizanska cesta 12, SI-3320 Velenje, Slovenia.

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Summary
This summary is machine-generated.

This study introduces user population re-targeting to align machine object recognition with human understanding. Adapting AI outputs to user preferences significantly improves categorization relevance, enhancing human-AI interaction.

Keywords:
categorizationclassificationcognitive relevancecrowd-sourcingdeep learningtarget user population

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Area of Science:

  • Computer Science
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Machine object recognition accuracy is often hindered by discrepancies between dataset labels and human perception.
  • Current AI classification systems may not align with user-friendly categorization preferences.
  • Bridging the gap between machine-generated labels and human understanding is crucial for effective AI deployment.

Purpose of the Study:

  • To develop a methodology for adapting AI object recognition outputs to a target user population.
  • To introduce the concept of 'user population re-targeting' and the 'Cognitive Relevance Transform'.
  • To evaluate the effectiveness of re-targeting in improving categorization relevance for human users.

Main Methods:

  • Developed a methodology for adapting pre-trained object classification algorithms to target populations.
  • Designed population tests to gather data on user-preferred categorization.
  • Introduced the 'Cognitive Relevance Transform' to bridge dataset-bound and population-specific categorizations.

Main Results:

  • Experiments demonstrated that the target population significantly preferred the re-targeted categorization.
  • The study suggests human observer performance in object recognition might be underestimated.
  • Re-targeting outcomes can be unpredictable without empirical testing on the target population.

Conclusions:

  • User population re-targeting offers a viable solution to align AI object recognition with human perception.
  • The 'Cognitive Relevance Transform' is a key component in achieving this alignment.
  • Future AI development should incorporate user-centric testing for improved relevance and performance.