Novel cross-dimensional coarse-fine-grained complementary network for image-text matching.

Area of Science:

• Computer Science
• Artificial Intelligence
• Multimodal Machine Learning

Background:

• Multimodal applications like image-text matching face challenges due to the heterogeneity gap between visual and textual data.
• Existing methods often fail to integrate fine-grained word-region matching with coarse-grained image-text matching and ignore feature dimension differences.

Purpose of the Study:

• To propose a novel network, the Cross-Dimensional Coarse-Fine-Grained Complementary Network (CDGCN), to overcome limitations in current image-text matching techniques.
• To enhance semantic consistency and holistic understanding in multimodal applications by bridging the gap between local and global feature representations.

Main Methods:

• The CDGCN employs fine-grained semantic alignment of image regions and text words using cross-dimensional dependencies.
• A Coarse-Grained Cross-Dimensional Semantic Aggregation (CGDSA) module complements local alignment with global image-text matching, aggregating features across and within dimensions.

Main Results:

• The CDGCN demonstrated substantial performance improvements on the Flickr30K and MS-COCO datasets.
• The proposed method achieved performance increments ranging from 7.7% to 16% compared to state-of-the-art methods.

Conclusions:

• The CDGCN effectively addresses the semantic gap and heterogeneity in multimodal data by integrating complementary matching strategies.
• The network's ability to preserve semantic integrity through cross-dimensional and coarse-fine-grained feature aggregation leads to significant advancements in image-text matching accuracy.