Image-Text Embedding Learning via Visual and Textual Semantic Reasoning.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Cross-modal retrieval between images and text is challenging due to semantic gaps in current image representations.
• Existing methods often struggle to capture comprehensive semantic concepts within image-text alignments.

Purpose of the Study:

• To develop an intuitive and interpretable model for learning a common embedding space for image-text alignment.
• To address the limitations of current image representations by incorporating semantic relationship reasoning.

Main Methods:

• Incorporating semantic relationship information into visual and textual features via region or word relationship reasoning.
• Utilizing gate and memory mechanisms for global semantic reasoning on enhanced features.
• Learning alignment to capture key objects and semantic concepts in visual representations.

Main Results:

• The model successfully captures key objects and semantic concepts, aligning visual representations with text descriptions.
• Experiments on MS-COCO and Flickr30K datasets show superior performance compared to state-of-the-art methods.
• The proposed method demonstrates significant efficiency gains, being 30-75 times faster than recent approaches.

Conclusions:

• The developed framework effectively learns global representations through visual semantic reasoning, enabling high performance with simple matching strategies.
• The model offers a highly efficient and effective solution for cross-modal retrieval, outperforming complex local matching strategies.