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Related Experiment Video

Updated: May 18, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Object-centric image editing via position-structure guided diffusion.

Qi Si1, Xiangrui Chen1, Bo Wang2

  • 1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 16, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for precise object editing in complex images using latent diffusion models. The method enhances object localization and preserves background integrity for superior image manipulation results.

Keywords:
Attention guidanceDiffusion modelEnergy-guided latent optimizationObject-centric editingTraining-free

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Last Updated: May 18, 2026

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Published on: August 30, 2013

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Object-centric editing in complex scenes is challenging due to inaccurate localization and interference in diffusion models.
  • Current methods struggle with precise object placement and maintaining background integrity during edits.

Purpose of the Study:

  • To develop a training-free framework for fine-grained object-centric image editing.
  • To address challenges in object localization and contextual integrity preservation.

Main Methods:

  • A latent space optimization strategy refines cross-attention maps to disentangle object representations and improve spatial alignment.
  • A region-aware fusion mechanism adaptively blends edited features with background information to prevent structural distortion.

Main Results:

  • The proposed method demonstrates superior performance compared to state-of-the-art approaches on public benchmarks.
  • Achieved significant improvements in both structural fidelity and semantic coherence of edited images.

Conclusions:

  • The novel framework effectively tackles key challenges in object-centric image editing.
  • Offers enhanced control and accuracy for complex image manipulation tasks.