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Related Experiment Video

Updated: Sep 13, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Hierarchical knowledge-guided reasoning for text-based person re-identification.

Ruigeng Zeng¹, Wentao Ma², Tongqing Zhou³

¹Laboratory of Digitizing Software for Frontier Equipment, National University of Defence Technology, Changsha, 410073, Hunan, China; National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Changsha, 410073, Hunan, China; School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, Anhui, China.

Neural Networks : the Official Journal of the International Neural Network Society

|July 27, 2025

Summary

This summary is machine-generated.

This study introduces Hierarchical Knowledge-Guided Reasoning (HKGR) to improve text-image person re-identification (TIReID) by using scene graph knowledge for better visual-textual alignment, achieving state-of-the-art results.

Keywords:

Knowledge-guided reasoning Scene graph Text-image person re-identification

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Masked language modeling (MLM) has advanced text-image person re-identification (TIReID) but struggles with random token masking, hindering precise visual-textual alignment.
Current MLM approaches in TIReID may overlook the semantic nuances required for effective fine-grained alignment by treating all sub-words equally.

Purpose of the Study:

To enhance TIReID by leveraging hierarchical scene graph knowledge within text to guide token masking.
To improve cross-modal representation and overcome the limitations of random token selection in MLM for TIReID.

Main Methods:

The proposed Hierarchical Knowledge-Guided Reasoning (HKGR) framework utilizes object, attribute, and relation-level masking based on textual scene graph knowledge.
A Multi-Grained Semantic Alignment (MGA) module is introduced to refine image-text alignment using token selection and similarity distribution constraints.

Main Results:

The HKGR framework achieved state-of-the-art (SoTA) performance across three public benchmark datasets.
The method demonstrated superior performance in all evaluation metrics for TIReID tasks.

Conclusions:

The HKGR framework effectively improves TIReID by incorporating hierarchical knowledge for guided visual-textual alignment.
The knowledge-guided approach shows promise for broader applications in multi-modal research, including cross-modal retrieval and visual question answering.