arXiv:2601.04571v1 Announce Type: cross
Abstract: Multimodal retrieval has emerged as a promising yet challenging research direction in recent years. Most existing studies in multimodal retrieval focus on capturing information in multimodal data that is similar to their paired texts, but often ignores the complementary information contained in multimodal data. In this study, we propose CIEA, a novel multimodal retrieval approach that employs Complementary Information Extraction and Alignment, which transforms both text and images in documents into a unified latent space and features a complementary information extractor designed to identify and preserve differences in the image representations. We optimize CIEA using two complementary contrastive losses to ensure semantic integrity and effectively capture the complementary information contained in images. Extensive experiments demonstrate the effectiveness of CIEA, which achieves significant improvements over both divide-and-conquer models and universal dense retrieval models. We provide an ablation study, further discussions, and case studies to highlight the advancements achieved by CIEA. To promote further research in the community, we have released the source code at https://github.com/zengdlong/CIEA.
Expert Commentary:
As an expert commentator on multimedia information systems, I find the concept of multimodal retrieval to be crucial in today’s digital age. The study mentioned in the abstract highlights the importance of capturing not only similar information but also complementary information in multimodal data. This is where the interdisciplinary nature of this research comes into play. The use of Complementary Information Extraction and Alignment (CIEA) to transform text and images into a unified latent space is a novel approach that combines concepts from artificial reality, augmented reality, and virtual realities.
By optimizing CIEA with contrastive losses, the study ensures semantic integrity and effectively captures the complementary information contained in images. This approach not only improves upon existing models but also showcases the potential for further advancements in multimedia retrieval systems. The release of the source code on GitHub further promotes collaboration and innovation in the field.
Related Concepts:
- Animations: The use of CIEA to align text and images can be likened to creating animations where different elements come together to form a cohesive narrative.
- Artificial Reality: The transformation of data into a unified latent space parallels the creation of artificial realities where the virtual and real worlds intersect.
- Augmented Reality: By extracting and aligning complementary information, CIEA can enhance the user experience similar to augmented reality technologies.
- Virtual Realities: The optimization of CIEA using contrastive losses mirrors the process of creating immersive virtual realities that prioritize user engagement.
In conclusion, the study on CIEA showcases the potential of multi-disciplinary approaches in advancing multimedia information systems. By focusing on capturing complementary information, researchers can enhance the retrieval of multimodal data and pave the way for future innovations in the field.