An Optimization Algorithm for Multimodal Data Alignment
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913728510820352 |
|---|---|
| author | Zhang, Wei Wang, Xinyue Yu, Lan Li, Shi |
| author_facet | Zhang, Wei Wang, Xinyue Yu, Lan Li, Shi |
| contents | In the data era, the integration of multiple data types, known as multimodality, has become a key area of interest in the research community. This interest is driven by the goal to develop cutting edge multimodal models capable of serving as adaptable reasoning engines across a wide range of modalities and domains. Despite the fervent development efforts, the challenge of optimally representing different forms of data within a single unified latent space a crucial step for enabling effective multimodal reasoning has not been fully addressed. To bridge this gap, we introduce AlignXpert, an optimization algorithm inspired by Kernel CCA crafted to maximize the similarities between N modalities while imposing some other constraints. This work demonstrates the impact on improving data representation for a variety of reasoning tasks, such as retrieval and classification, underlining the pivotal importance of data representation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Optimization Algorithm for Multimodal Data Alignment Zhang, Wei Wang, Xinyue Yu, Lan Li, Shi Machine Learning Artificial Intelligence In the data era, the integration of multiple data types, known as multimodality, has become a key area of interest in the research community. This interest is driven by the goal to develop cutting edge multimodal models capable of serving as adaptable reasoning engines across a wide range of modalities and domains. Despite the fervent development efforts, the challenge of optimally representing different forms of data within a single unified latent space a crucial step for enabling effective multimodal reasoning has not been fully addressed. To bridge this gap, we introduce AlignXpert, an optimization algorithm inspired by Kernel CCA crafted to maximize the similarities between N modalities while imposing some other constraints. This work demonstrates the impact on improving data representation for a variety of reasoning tasks, such as retrieval and classification, underlining the pivotal importance of data representation. |
| title | An Optimization Algorithm for Multimodal Data Alignment |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.07636 |