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Related Experiment Video

Updated: Jul 20, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Composed Image Retrieval via Cross Relation Network With Hierarchical Aggregation Transformer.

Qu Yang, Mang Ye, Zhaohui Cai

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 2, 2023

Summary

This study introduces a new method for Composing Text and Image to Image Retrieval (CTI-IR) that handles multiple text functions. The Cross Relation Network (CRN) unifies text relevance and modification for better image retrieval.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Composing Text and Image to Image Retrieval (CTI-IR) seeks images matching both visual and semantic query criteria.
Existing methods often overlook the diverse roles of reference text, such as modification and auxiliary functions.

Purpose of the Study:

To develop a unified framework for CTI-IR that effectively models multiple text functions simultaneously.
To address the limitations of existing approaches in handling complex text-image relationships.

Main Methods:

A novel Hierarchical Aggregation Transformer incorporated with a Cross Relation Network (CRN) was proposed.
The CRN unifies modification and relevance in a single framework, enabling simultaneous modeling of modification, auxiliary text, or their combinations.
The Hierarchical Aggregation Transformer aggregates features hierarchically to mitigate information loss.

Main Results:

The proposed CRN demonstrated superior performance across three fashion-domain datasets.
The unified framework showed broader applicability in modeling various composed retrieval scenarios.

Conclusions:

The CRN offers a more comprehensive and flexible solution for CTI-IR by effectively handling diverse text functions.
This approach advances the state-of-the-art in text-guided image retrieval, particularly in complex scenarios.