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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Related Experiment Video

Updated: Jun 21, 2026

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
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Manipulation Direction: Evaluating Text-Guided Image Manipulation Based on Similarity between Changes in Image and

Yuto Watanabe1, Ren Togo2, Keisuke Maeda2

  • 1Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
Summary

This study introduces Manipulation Direction (MD), a new metric for evaluating text-guided image manipulation. MD assesses how well image changes align with text descriptions, offering a more robust performance measure.

Keywords:
evaluation metricgenerative adversarial networkmanipulation directiontext-guided image manipulation

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Text-guided image manipulation aims to alter images based on textual input while preserving unchanged regions.
  • Existing research has focused on improving manipulation techniques, but performance evaluation methods are lacking.

Purpose of the Study:

  • To propose a novel, logical, and robust metric for evaluating text-guided image manipulation performance.
  • To address the inadequacy in current performance evaluation research for text-guided image manipulation.

Main Methods:

  • The study introduces Manipulation Direction (MD), a metric focused on the consistency of changes between image and text modalities before and after manipulation.
  • MD quantifies how well the image transformation aligns with the provided text guidance.

Main Results:

  • Experiments on Multi-Modal-CelebA-HQ and Caltech-UCSD Birds datasets demonstrated a strong correlation between MD scores and human subjective evaluations.
  • The proposed MD metric showed superior performance compared to existing evaluation metrics.

Conclusions:

  • Manipulation Direction (MD) provides a comprehensive and reliable method for assessing the quality of text-guided image manipulation.
  • The metric effectively evaluates the alignment between image modifications and textual instructions, outperforming current standards.