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Related Experiment Video

Updated: Jan 12, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Fourier-based adaptive counterfactual intervention for object re-identification.

Haijun Xiong1, Bin Feng1, Bang Wang1

  • 1School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Fourier-Based Adaptive Counterfactual Intervention (FACI-ReID), a new method for object re-identification (ReID). FACI-ReID effectively disentangles identity features from background noise, significantly improving ReID accuracy.

Keywords:
Adaptive learningCounterfactual interventionFourier transformObject re-identification

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Object Re-Identification (ReID) is crucial for retrieving objects across images.
  • Non-identity cues (e.g., background, color) often hinder accurate ReID by entangling identity representations.
  • Existing methods face challenges in spatially disentangling these identity-specific features.

Purpose of the Study:

  • To propose a novel method, Fourier-Based Adaptive Counterfactual Intervention (FACI-ReID), for enhanced object ReID.
  • To effectively address the challenge of disentangling identity-intrinsic features from confounding factors in images.
  • To improve the discriminative power of learned features for robust object retrieval.

Main Methods:

  • Developed FACI-ReID, incorporating a Counterfactual Intervention Module (CIM) and Fourier-based Selective Attention (FSA).
  • Employed FSA modules utilizing the Fourier transform to extract factual and counterfactual features, fusing frequency-domain information.
  • Implemented CIM to minimize confounder influence by maximizing feature likelihood differences, focusing on identity cues.

Main Results:

  • FACI-ReID demonstrated superior performance across five benchmark datasets compared to state-of-the-art methods.
  • The method successfully learned more discriminative features by reducing the impact of non-identity cues.
  • Counterfactual intervention in the frequency domain preserved essential identity-intrinsic information.

Conclusions:

  • FACI-ReID offers an effective approach to object ReID by leveraging frequency-domain analysis and counterfactual intervention.
  • The proposed method significantly enhances the ability to distinguish objects based on their intrinsic identity.
  • The findings highlight the potential of frequency-domain manipulation for improving computer vision tasks like ReID.