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Dual-stage framework with soft-label distillation and spatial prompting for image-text retrieval
Ran Jin1,2, Zhengang Li1, Fang Deng1
1Zhejiang Wanli University, School of Big Data and Software Engineering, Ningbo, China.
Plos One
|October 10, 2025
Summary
This study introduces a dual-stage training framework to improve image-text retrieval accuracy by addressing inter-modal matching and fine-grained localization deficiencies using Soft Label Distillation (SLD) and Spatial Text Prompt (STP). The novel approach enhances cross-modal understanding for better retrieval performance.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Vision-language pre-training (VLP) has advanced cross-modal tasks.
- Current image-text retrieval methods suffer from inter-modal matching and fine-grained localization deficiencies, limiting accuracy.
Purpose of the Study:
- To propose a novel dual-stage training framework to overcome limitations in image-text retrieval.
- To enhance the accuracy and fine-grained alignment in cross-modal retrieval tasks.
Main Methods:
- Implemented a dual-stage training framework.
- Utilized Soft Label Distillation (SLD) in the first stage to align image-text contrastive relationships and mitigate overfitting.
- Introduced Spatial Text Prompt (STP) in the second stage to improve visual grounding and fine-grained alignment.
Main Results:
- The proposed method significantly outperforms state-of-the-art approaches on standard image-text retrieval datasets.
- Demonstrated improved accuracy in cross-modal retrieval tasks through enhanced alignment.
Conclusions:
- The dual-stage training framework effectively addresses key challenges in image-text retrieval.
- The combination of SLD and STP leads to superior performance in cross-modal understanding and retrieval.
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