Cross-modal learning to rank via latent joint representation

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Cross-modal ranking is crucial for applications using multimodal data.
• Effective cross-media retrieval (e.g., image-to-text) requires joint data representation and ranking functions.

Purpose of the Study:

• To propose a novel approach for discovering latent joint representations of multimodal data.
• To develop a listwise ranking method for enhancing cross-media retrieval performance.

Main Methods:

• Utilized a conditional random field and structural learning for latent joint representation discovery.
• Developed cross-modal learning to rank via latent joint representation (CML²R).
• Captured correlations via shared hidden variables (topics) for a hidden-topic-driven ranking function.

Main Results:

• The proposed CML²R approach demonstrated strong performance in cross-media retrieval tasks.
• The method successfully learned discriminative representations of multimodal data.
• Experimental results validate the effectiveness of the latent joint representation learning.

Conclusions:

• CML²R offers an effective solution for cross-media retrieval by learning joint representations.
• The approach leverages hidden topics to drive a discriminative ranking function.
• This work advances the field of multimodal data analysis and retrieval.