Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification

2026-06-01 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, medium

Summary

Karina Kvanchiani and Timur Mamedov address the optimization conflicts in jointly training image-based (I2I) and text-based (T2I) person re-identification (ReID) systems. These tasks have differing objectives: I2I ReID seeks identity-level invariance across images, while T2I ReID focuses on instance-specific textual descriptions, leading to suboptimal shared representations. The authors propose a decoupled two-stage training pipeline that utilizes a single vision encoder to support both I2I and T2I retrieval, specifically designed to prevent cross-task interference during training. Extensive experiments across various configurations, including domain mixing and learning strategies, demonstrated that I2I ReID pre-training positively impacts the generalization ability to T2I data. Furthermore, incorporating textual supervision during the vision encoder's training stage was found to enhance both I2I and T2I performance, suggesting a meaningful step towards unified ReID systems and cross-modal retrieval.

Key takeaway

For machine learning engineers developing unified person re-identification systems, you should consider adopting a decoupled two-stage training pipeline. This approach pre-trains vision encoders with image-based ReID tasks. It then integrates textual supervision, effectively mitigating optimization conflicts between image- and text-based objectives. Implementing this strategy can significantly improve the generalization and overall performance of your cross-modal retrieval models, leading to more robust and accurate deployments.

Key insights

Decoupling I2I and T2I ReID training with a two-stage pipeline resolves optimization conflicts for better shared representations.

Principles

• I2I and T2I ReID have conflicting optimization objectives.
• I2I pre-training improves T2I generalization.
• Textual supervision enhances both I2I and T2I.

Method

A decoupled two-stage training pipeline uses a single vision encoder for I2I and T2I retrieval, preventing cross-task interference.

In practice

• Pre-train vision encoders with I2I data first.
• Integrate textual supervision early in training.
• Design ReID systems with decoupled stages.

Topics

• Person Re-identification
• Cross-modal Retrieval
• Vision-Language Models
• Deep Learning Optimization
• Image-Text Alignment
• Multi-task Learning

Code references

• Zplusdragon/PLIP

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.