Year: 2026
Type: article
Abstract: Cross-modal retrieval, particularly image-text matching, is crucial in multimedia analysis and artificial intelligence, with applications in intelligent search and human-computer interaction. Current methods often overlook the rich semantic relationships between labels, leading to limited discriminability. We introduce a Two-Layer Graph Convolutional Network (L2-GCN) to model label correlations and a hybrid loss function, Circle-Soft, to enhance alignment and discriminability. Extensive experiments on the NUS-WIDE, MIRFlickr, and MS-COCO datasets demonstrate the effectiveness of our approach. The results show that the proposed method consistently outperforms current baselines, achieving accuracy improvements of 0.5%, 0.5%, and 1.0%, respectively. The source code is accessible via https://github.com/buzzcut619/L2-GCN-CIRCLE-SOFT.
Source: Scientific Reports
Authors: Lin Wang, Chenchen Wang, Simin Peng
Institution: Yancheng Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G8982842057
OpenAlex ID: W7125779453