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Updated: Aug 7, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

HAAN: Learning a Hierarchical Adaptive Alignment Network for Image-Text Retrieval.

Shuhuai Wang¹, Zheng Liu^1,2, Xinlei Pei^1,2

¹School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This study introduces a novel hierarchical adaptive alignment network for improved image-text retrieval. The method effectively fuses multi-level image and text data, enhancing semantic associations for better cross-modal search results.

Keywords:

adaptive weighted loss global-level alignment image-text retrieval local-level alignment

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Area of Science:

Computer Science
Artificial Intelligence
Information Retrieval

Background:

Image-text retrieval is crucial for cross-modal search but faces challenges due to modality and granularity differences.
Existing methods often fail to fully exploit complementarities between images and texts at various levels.

Purpose of the Study:

To propose a novel hierarchical adaptive alignment network for effective image-text retrieval.
To address the limitations of existing methods in mining and fusing multi-level image-text semantic associations.

Main Methods:

Developed a multi-level alignment network to mine both global-level and local-level image and text data.
Introduced an adaptive weighted loss function for flexible, two-stage optimization of image-text similarity.
Validated the approach through extensive experiments on benchmark datasets.

Main Results:

The proposed method significantly enhances semantic association between images and texts.
Experimental results on Corel 5K, Pascal Sentence, and Wiki datasets demonstrate superior performance.
Outperformed eleven state-of-the-art methods in image-text retrieval tasks.

Conclusions:

The hierarchical adaptive alignment network effectively addresses challenges in image-text retrieval.
The method's ability to mine and fuse multi-level information leads to improved cross-modal search.
The proposed approach offers a promising direction for future research in this domain.