Learning Domain Invariant Representations for Generalizable Person Re-Identification.

Area of Science:

• Computer Vision
• Machine Learning

Background:

• Generalizable person Re-Identification (ReID) aims to develop models that perform well across diverse datasets without retraining.
• Existing methods often struggle with domain shift, limiting their real-world applicability.

Purpose of the Study:

• To propose a novel framework, Domain Invariant Representation Learning for generalizable person Re-Identification (DIR-ReID), for robust cross-domain person ReID.
• To leverage causal analysis to disentangle identity-specific and domain-specific factors.

Main Methods:

• Constructed a structural causal model (SCM) to understand relationships between identity, identity-specific factors, and domain-specific factors.
• Developed DIR-ReID to disentangle these factors into independent feature spaces.
• Implemented a backdoor adjustment approximation as a causal intervention.

Main Results:

• DIR-ReID demonstrated superior performance compared to state-of-the-art methods.
• The framework achieved strong results on large-scale domain generalization (DG) ReID benchmarks.
• Disentangling factors led to more generalizable representations.

Conclusions:

• The proposed DIR-ReID framework effectively addresses the domain generalization challenge in person ReID.
• Causal inference provides a powerful approach for learning invariant representations.
• This work advances the capabilities of computer vision systems for person recognition in uncontrolled environments.